{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"“first_steps_with_tensor_flow.ipynb”的副本","version":"0.3.2","views":{},"default_view":{},"provenance":[{"file_id":"/v2/external/notebooks/mlcc/first_steps_with_tensor_flow.ipynb","timestamp":1520097700300}],"collapsed_sections":["ajVM7rkoYXeL","ci1ISxxrZ7v0","copyright-notice"],"toc_visible":true}},"cells":[{"metadata":{"id":"copyright-notice","colab_type":"text"},"cell_type":"markdown","source":["#### Copyright 2017 Google LLC."]},{"metadata":{"id":"copyright-notice2","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}},"cellView":"both"},"cell_type":"code","source":["# Licensed under the Apache License, Version 2.0 (the \"License\");\n","# you may not use this file except in compliance with the License.\n","# You may obtain a copy of the License at\n","#\n","# https://www.apache.org/licenses/LICENSE-2.0\n","#\n","# Unless required by applicable law or agreed to in writing, software\n","# distributed under the License is distributed on an \"AS IS\" BASIS,\n","# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","# See the License for the specific language governing permissions and\n","# limitations under the License."],"execution_count":0,"outputs":[]},{"metadata":{"id":"4f3CKqFUqL2-","colab_type":"text","slideshow":{"slide_type":"slide"}},"cell_type":"markdown","source":[" # 使用 TensorFlow 的基本步骤"]},{"metadata":{"id":"Bd2Zkk1LE2Zr","colab_type":"text"},"cell_type":"markdown","source":[" **学习目标：**\n","  * 学习基本的 TensorFlow 概念\n","  * 在 TensorFlow 中使用 `LinearRegressor` 类并基于单个输入特征预测各城市街区的房屋价值中位数\n","  * 使用均方根误差 (RMSE) 评估模型预测的准确率\n","  * 通过调整模型的超参数提高模型准确率"]},{"metadata":{"id":"MxiIKhP4E2Zr","colab_type":"text"},"cell_type":"markdown","source":[" 数据基于加利福尼亚州 1990 年的人口普查数据。"]},{"metadata":{"id":"6TjLjL9IU80G","colab_type":"text"},"cell_type":"markdown","source":[" ## 设置\n","在此第一个单元格中，我们将加载必要的库。"]},{"metadata":{"id":"rVFf5asKE2Zt","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[],"base_uri":"https://localhost:8080/","height":17},"outputId":"59dd348a-da98-4a99-f537-1f2a531f90ae","executionInfo":{"status":"ok","timestamp":1520096952161,"user_tz":-480,"elapsed":1175,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["import math\n","\n","from IPython import display\n","from matplotlib import cm\n","from matplotlib import gridspec\n","from matplotlib import pyplot as plt\n","import numpy as np\n","import pandas as pd\n","from sklearn import metrics\n","import tensorflow as tf\n","from tensorflow.python.data import Dataset\n","\n","tf.logging.set_verbosity(tf.logging.ERROR)\n","pd.options.display.max_rows = 10\n","pd.options.display.float_format = '{:.1f}'.format"],"execution_count":13,"outputs":[]},{"metadata":{"id":"ipRyUHjhU80Q","colab_type":"text"},"cell_type":"markdown","source":[" 接下来，我们将加载数据集。"]},{"metadata":{"id":"9ivCDWnwE2Zx","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[],"base_uri":"https://localhost:8080/","height":17},"outputId":"21e67032-8907-4fda-c456-f33049ffc9a6","executionInfo":{"status":"ok","timestamp":1520096955839,"user_tz":-480,"elapsed":1236,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["california_housing_dataframe = pd.read_csv(\"https://storage.googleapis.com/ml_universities/california_housing_train.csv\", sep=\",\")"],"execution_count":14,"outputs":[]},{"metadata":{"id":"vVk_qlG6U80j","colab_type":"text"},"cell_type":"markdown","source":[" 我们将对数据进行随机化处理，以确保不会出现任何病态排序结果（可能会损害随机梯度下降法的效果）。此外，我们会将 `median_house_value` 调整为以千为单位，这样，模型就能够以常用范围内的学习速率较为轻松地学习这些数据。"]},{"metadata":{"id":"r0eVyguIU80m","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[{"item_id":1}],"base_uri":"https://localhost:8080/","height":439},"outputId":"5135f010-5dee-41b6-edd7-aaf5984ac952","executionInfo":{"status":"ok","timestamp":1520096957460,"user_tz":-480,"elapsed":982,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["california_housing_dataframe = california_housing_dataframe.reindex(\n","    np.random.permutation(california_housing_dataframe.index))\n","california_housing_dataframe[\"median_house_value\"] /= 1000.0\n","california_housing_dataframe"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      <th>households</th>\n","      <th>median_income</th>\n","      <th>median_house_value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>10048</th>\n","      <td>-119.8</td>\n","      <td>36.8</td>\n","      <td>52.0</td>\n","      <td>2408.0</td>\n","      <td>498.0</td>\n","      <td>1361.0</td>\n","      <td>465.0</td>\n","      <td>2.1</td>\n","      <td>61.3</td>\n","    </tr>\n","    <tr>\n","      <th>5354</th>\n","      <td>-118.2</td>\n","      <td>34.1</td>\n","      <td>52.0</td>\n","      <td>1000.0</td>\n","      <td>192.0</td>\n","      <td>363.0</td>\n","      <td>158.0</td>\n","      <td>4.3</td>\n","      <td>352.8</td>\n","    </tr>\n","    <tr>\n","      <th>4491</th>\n","      <td>-118.0</td>\n","      <td>34.0</td>\n","      <td>42.0</td>\n","      <td>1430.0</td>\n","      <td>338.0</td>\n","      <td>1269.0</td>\n","      <td>321.0</td>\n","      <td>3.3</td>\n","      <td>148.8</td>\n","    </tr>\n","    <tr>\n","      <th>10704</th>\n","      <td>-120.6</td>\n","      <td>37.3</td>\n","      <td>18.0</td>\n","      <td>5009.0</td>\n","      <td>826.0</td>\n","      <td>2497.0</td>\n","      <td>805.0</td>\n","      <td>4.2</td>\n","      <td>146.3</td>\n","    </tr>\n","    <tr>\n","      <th>9085</th>\n","      <td>-119.0</td>\n","      <td>35.3</td>\n","      <td>35.0</td>\n","      <td>1576.0</td>\n","      <td>405.0</td>\n","      <td>870.0</td>\n","      <td>282.0</td>\n","      <td>1.7</td>\n","      <td>59.5</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>14687</th>\n","      <td>-122.2</td>\n","      <td>37.8</td>\n","      <td>36.0</td>\n","      <td>1047.0</td>\n","      <td>214.0</td>\n","      <td>651.0</td>\n","      <td>166.0</td>\n","      <td>1.7</td>\n","      <td>82.1</td>\n","    </tr>\n","    <tr>\n","      <th>14317</th>\n","      <td>-122.1</td>\n","      <td>37.7</td>\n","      <td>30.0</td>\n","      <td>2599.0</td>\n","      <td>366.0</td>\n","      <td>922.0</td>\n","      <td>350.0</td>\n","      <td>5.8</td>\n","      <td>330.2</td>\n","    </tr>\n","    <tr>\n","      <th>6686</th>\n","      <td>-118.3</td>\n","      <td>34.1</td>\n","      <td>38.0</td>\n","      <td>380.0</td>\n","      <td>130.0</td>\n","      <td>445.0</td>\n","      <td>140.0</td>\n","      <td>1.9</td>\n","      <td>137.5</td>\n","    </tr>\n","    <tr>\n","      <th>15870</th>\n","      <td>-122.4</td>\n","      <td>37.8</td>\n","      <td>52.0</td>\n","      <td>1974.0</td>\n","      <td>525.0</td>\n","      <td>935.0</td>\n","      <td>465.0</td>\n","      <td>2.7</td>\n","      <td>300.0</td>\n","    </tr>\n","    <tr>\n","      <th>11947</th>\n","      <td>-121.4</td>\n","      <td>38.4</td>\n","      <td>18.0</td>\n","      <td>2643.0</td>\n","      <td>502.0</td>\n","      <td>1755.0</td>\n","      <td>541.0</td>\n","      <td>3.3</td>\n","      <td>91.2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>17000 rows × 9 columns</p>\n","</div>"],"text/plain":["       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n","10048     -119.8      36.8                52.0       2408.0           498.0   \n","5354      -118.2      34.1                52.0       1000.0           192.0   \n","4491      -118.0      34.0                42.0       1430.0           338.0   \n","10704     -120.6      37.3                18.0       5009.0           826.0   \n","9085      -119.0      35.3                35.0       1576.0           405.0   \n","...          ...       ...                 ...          ...             ...   \n","14687     -122.2      37.8                36.0       1047.0           214.0   \n","14317     -122.1      37.7                30.0       2599.0           366.0   \n","6686      -118.3      34.1                38.0        380.0           130.0   \n","15870     -122.4      37.8                52.0       1974.0           525.0   \n","11947     -121.4      38.4                18.0       2643.0           502.0   \n","\n","       population  households  median_income  median_house_value  \n","10048      1361.0       465.0            2.1                61.3  \n","5354        363.0       158.0            4.3               352.8  \n","4491       1269.0       321.0            3.3               148.8  \n","10704      2497.0       805.0            4.2               146.3  \n","9085        870.0       282.0            1.7                59.5  \n","...           ...         ...            ...                 ...  \n","14687       651.0       166.0            1.7                82.1  \n","14317       922.0       350.0            5.8               330.2  \n","6686        445.0       140.0            1.9               137.5  \n","15870       935.0       465.0            2.7               300.0  \n","11947      1755.0       541.0            3.3                91.2  \n","\n","[17000 rows x 9 columns]"]},"metadata":{"tags":[]},"execution_count":15}]},{"metadata":{"id":"HzzlSs3PtTmt","colab_type":"text","slideshow":{"slide_type":"-"}},"cell_type":"markdown","source":[" ## 检查数据\n","\n","建议您在使用数据之前，先对它有一个初步的了解。\n","\n","我们会输出关于各列的一些实用统计信息快速摘要：样本数、均值、标准偏差、最大值、最小值和各种分位数。"]},{"metadata":{"id":"gzb10yoVrydW","colab_type":"code","slideshow":{"slide_type":"slide"},"colab":{"autoexec":{"startup":false,"wait_interval":0},"test":{"output":"ignore","timeout":600},"output_extras":[{"item_id":1}],"base_uri":"https://localhost:8080/","height":317},"cellView":"both","outputId":"8340afba-16c0-4461-c687-590729e0e948","executionInfo":{"status":"ok","timestamp":1520096962645,"user_tz":-480,"elapsed":1299,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["california_housing_dataframe.describe()"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>longitude</th>\n","      <th>latitude</th>\n","      <th>housing_median_age</th>\n","      <th>total_rooms</th>\n","      <th>total_bedrooms</th>\n","      <th>population</th>\n","      <th>households</th>\n","      <th>median_income</th>\n","      <th>median_house_value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>-119.6</td>\n","      <td>35.6</td>\n","      <td>28.6</td>\n","      <td>2643.7</td>\n","      <td>539.4</td>\n","      <td>1429.6</td>\n","      <td>501.2</td>\n","      <td>3.9</td>\n","      <td>207.3</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>2.0</td>\n","      <td>2.1</td>\n","      <td>12.6</td>\n","      <td>2179.9</td>\n","      <td>421.5</td>\n","      <td>1147.9</td>\n","      <td>384.5</td>\n","      <td>1.9</td>\n","      <td>116.0</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-124.3</td>\n","      <td>32.5</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>0.5</td>\n","      <td>15.0</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>-121.8</td>\n","      <td>33.9</td>\n","      <td>18.0</td>\n","      <td>1462.0</td>\n","      <td>297.0</td>\n","      <td>790.0</td>\n","      <td>282.0</td>\n","      <td>2.6</td>\n","      <td>119.4</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>-118.5</td>\n","      <td>34.2</td>\n","      <td>29.0</td>\n","      <td>2127.0</td>\n","      <td>434.0</td>\n","      <td>1167.0</td>\n","      <td>409.0</td>\n","      <td>3.5</td>\n","      <td>180.4</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>-118.0</td>\n","      <td>37.7</td>\n","      <td>37.0</td>\n","      <td>3151.2</td>\n","      <td>648.2</td>\n","      <td>1721.0</td>\n","      <td>605.2</td>\n","      <td>4.8</td>\n","      <td>265.0</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>-114.3</td>\n","      <td>42.0</td>\n","      <td>52.0</td>\n","      <td>37937.0</td>\n","      <td>6445.0</td>\n","      <td>35682.0</td>\n","      <td>6082.0</td>\n","      <td>15.0</td>\n","      <td>500.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n","count    17000.0   17000.0             17000.0      17000.0         17000.0   \n","mean      -119.6      35.6                28.6       2643.7           539.4   \n","std          2.0       2.1                12.6       2179.9           421.5   \n","min       -124.3      32.5                 1.0          2.0             1.0   \n","25%       -121.8      33.9                18.0       1462.0           297.0   \n","50%       -118.5      34.2                29.0       2127.0           434.0   \n","75%       -118.0      37.7                37.0       3151.2           648.2   \n","max       -114.3      42.0                52.0      37937.0          6445.0   \n","\n","       population  households  median_income  median_house_value  \n","count     17000.0     17000.0        17000.0             17000.0  \n","mean       1429.6       501.2            3.9               207.3  \n","std        1147.9       384.5            1.9               116.0  \n","min           3.0         1.0            0.5                15.0  \n","25%         790.0       282.0            2.6               119.4  \n","50%        1167.0       409.0            3.5               180.4  \n","75%        1721.0       605.2            4.8               265.0  \n","max       35682.0      6082.0           15.0               500.0  "]},"metadata":{"tags":[]},"execution_count":16}]},{"metadata":{"id":"Lr6wYl2bt2Ep","colab_type":"text","slideshow":{"slide_type":"-"}},"cell_type":"markdown","source":[" ## 构建第一个模型\n","\n","在本练习中，我们将尝试预测 `median_house_value`，它将是我们的标签（有时也称为目标）。我们将使用 `total_rooms` 作为输入特征。\n","\n","**注意**：我们使用的是城市街区级别的数据，因此该特征表示相应街区的房间总数。\n","\n","为了训练模型，我们将使用 TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API 提供的 [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) 接口。此 API 负责处理大量低级别模型搭建工作，并会提供执行模型训练、评估和推理的便利方法。"]},{"metadata":{"id":"0cpcsieFhsNI","colab_type":"text"},"cell_type":"markdown","source":[" ### 第 1 步：定义特征并配置特征列"]},{"metadata":{"id":"EL8-9d4ZJNR7","colab_type":"text"},"cell_type":"markdown","source":[" 为了将我们的训练数据导入 TensorFlow，我们需要指定每个特征包含的数据类型。在本练习及今后的练习中，我们主要会使用以下两类数据：\n","\n","* **分类数据**：一种文字数据。在本练习中，我们的住房数据集不包含任何分类特征，但您可能会看到的示例包括家居风格以及房地产广告词。\n","\n","* **数值数据**：一种数字（整数或浮点数）数据以及您希望视为数字的数据。有时您可能会希望将数值数据（例如邮政编码）视为分类数据（我们将在稍后的部分对此进行详细说明）。\n","\n","在 TensorFlow 中，我们使用一种称为“**特征列**”的结构来表示特征的数据类型。特征列仅存储对特征数据的描述；不包含特征数据本身。\n","\n","一开始，我们只使用一个数值输入特征 `total_rooms`。以下代码会从 `california_housing_dataframe` 中提取 `total_rooms` 数据，并使用 `numeric_column` 定义特征列，这样会将其数据指定为数值："]},{"metadata":{"id":"rhEbFCZ86cDZ","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[{"item_id":1}],"base_uri":"https://localhost:8080/","height":272},"outputId":"bc48e143-3171-4610-bb86-ba708b5b8a12","executionInfo":{"status":"ok","timestamp":1520096966610,"user_tz":-480,"elapsed":983,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["# Define the input feature: total_rooms.\n","my_feature = california_housing_dataframe[[\"total_rooms\"]]\n","\n","# Configure a numeric feature column for total_rooms.\n","feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]\n","\n","print my_feature\n","print feature_columns"],"execution_count":17,"outputs":[{"output_type":"stream","text":["       total_rooms\n","10048       2408.0\n","5354        1000.0\n","4491        1430.0\n","10704       5009.0\n","9085        1576.0\n","...            ...\n","14687       1047.0\n","14317       2599.0\n","6686         380.0\n","15870       1974.0\n","11947       2643.0\n","\n","[17000 rows x 1 columns]\n","[_NumericColumn(key='total_rooms', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)]\n"],"name":"stdout"}]},{"metadata":{"id":"K_3S8teX7Rd2","colab_type":"text"},"cell_type":"markdown","source":[" **注意**：`total_rooms` 数据的形状是一维数组（每个街区的房间总数列表）。这是 `numeric_column` 的默认形状，因此我们不必将其作为参数传递。"]},{"metadata":{"id":"UMl3qrU5MGV6","colab_type":"text"},"cell_type":"markdown","source":[" ### 第 2 步：定义目标"]},{"metadata":{"id":"cw4nrfcB7kyk","colab_type":"text"},"cell_type":"markdown","source":[" 接下来，我们将定义目标，也就是 `median_house_value`。同样，我们可以从 `california_housing_dataframe` 中提取它："]},{"metadata":{"id":"l1NvvNkH8Kbt","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[{"item_id":1}],"base_uri":"https://localhost:8080/","height":221},"outputId":"cf0a3cec-5ae2-4615-fc22-dbe0d9963b05","executionInfo":{"status":"ok","timestamp":1520096970097,"user_tz":-480,"elapsed":983,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["# Define the label.\n","targets = california_housing_dataframe[\"median_house_value\"]\n","print targets"],"execution_count":18,"outputs":[{"output_type":"stream","text":["10048    61.3\n","5354    352.8\n","4491    148.8\n","10704   146.3\n","9085     59.5\n","         ... \n","14687    82.1\n","14317   330.2\n","6686    137.5\n","15870   300.0\n","11947    91.2\n","Name: median_house_value, Length: 17000, dtype: float64\n"],"name":"stdout"}]},{"metadata":{"id":"4M-rTFHL2UkA","colab_type":"text"},"cell_type":"markdown","source":[" ### 第 3 步：配置 LinearRegressor"]},{"metadata":{"id":"fUfGQUNp7jdL","colab_type":"text"},"cell_type":"markdown","source":[" 接下来，我们将使用 LinearRegressor 配置线性回归模型，并使用 `GradientDescentOptimizer`（它会实现小批量随机梯度下降法 (SGD)）训练该模型。`learning_rate` 参数可控制梯度步长的大小。\n","\n","**注意**：为了安全起见，我们还会通过 `clip_gradients_by_norm` 将[梯度裁剪](https://developers.google.com/machine-learning/glossary/#gradient_clipping)应用到我们的优化器。梯度裁剪可确保梯度大小在训练期间不会变得过大，梯度过大会导致梯度下降法失败。"]},{"metadata":{"id":"ubhtW-NGU802","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[],"base_uri":"https://localhost:8080/","height":17},"outputId":"6b2d7ea3-d4bc-4e53-cab4-0291b52feacc","executionInfo":{"status":"ok","timestamp":1520096972708,"user_tz":-480,"elapsed":1214,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["# Use gradient descent as the optimizer for training the model.\n","my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n","my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n","\n","# Configure the linear regression model with our feature columns and optimizer.\n","# Set a learning rate of 0.0000001 for Gradient Descent.\n","linear_regressor = tf.estimator.LinearRegressor(\n","    feature_columns=feature_columns,\n","    optimizer=my_optimizer\n",")"],"execution_count":19,"outputs":[]},{"metadata":{"id":"-0IztwdK2f3F","colab_type":"text"},"cell_type":"markdown","source":[" ### 第 4 步：定义输入函数"]},{"metadata":{"id":"S5M5j6xSCHxx","colab_type":"text"},"cell_type":"markdown","source":[" 要将加利福尼亚州住房数据导入 `LinearRegressor`，我们需要定义一个输入函数，让它告诉 TensorFlow 如何对数据进行预处理，以及在模型训练期间如何批处理、随机处理和重复数据。\n","\n","首先，我们将 *Pandas* 特征数据转换成 NumPy 数组字典。然后，我们可以使用 TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) 根据我们的数据构建 Dataset 对象，并将数据拆分成大小为 `batch_size` 的多批数据，以按照指定周期数 (num_epochs) 进行重复。\n","\n","**注意**：如果将默认值 `num_epochs=None` 传递到 `repeat()`，输入数据会无限期重复。\n","\n","然后，如果 `shuffle` 设置为 `True`，则我们会对数据进行随机处理，以便数据在训练期间以随机方式传递到模型。`buffer_size` 参数会指定 `shuffle` 将从中随机抽样的数据集的大小。\n","\n","最后，输入函数会为该数据集构建一个迭代器，并向 LinearRegressor 返回下一批数据。"]},{"metadata":{"id":"RKZ9zNcHJtwc","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[],"base_uri":"https://localhost:8080/","height":17},"outputId":"c3983457-3d47-49df-9b3c-997ccea1a77d","executionInfo":{"status":"ok","timestamp":1520096974976,"user_tz":-480,"elapsed":976,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n","    \"\"\"Trains a linear regression model of one feature.\n","  \n","    Args:\n","      features: pandas DataFrame of features\n","      targets: pandas DataFrame of targets\n","      batch_size: Size of batches to be passed to the model\n","      shuffle: True or False. Whether to shuffle the data.\n","      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n","    Returns:\n","      Tuple of (features, labels) for next data batch\n","    \"\"\"\n","  \n","    # Convert pandas data into a dict of np arrays.\n","    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n"," \n","    # Construct a dataset, and configure batching/repeating\n","    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n","    ds = ds.batch(batch_size).repeat(num_epochs)\n","    \n","    # Shuffle the data, if specified\n","    if shuffle:\n","      ds = ds.shuffle(buffer_size=10000)\n","    \n","    # Return the next batch of data\n","    features, labels = ds.make_one_shot_iterator().get_next()\n","    return features, labels"],"execution_count":20,"outputs":[]},{"metadata":{"id":"wwa6UeA1V5F_","colab_type":"text"},"cell_type":"markdown","source":[" **注意**：在后面的练习中，我们会继续使用此输入函数。有关输入函数和 `Dataset` API 的更详细的文档，请参阅 [TensorFlow 编程人员指南](https://www.tensorflow.org/programmers_guide/datasets)。"]},{"metadata":{"id":"4YS50CQb2ooO","colab_type":"text"},"cell_type":"markdown","source":[" ### 第 5 步：训练模型"]},{"metadata":{"id":"yP92XkzhU803","colab_type":"text"},"cell_type":"markdown","source":[" 现在，我们可以在 `linear_regressor` 上调用 `train()` 来训练模型。我们会将 `my_input_fn` 封装在 `lambda` 中，以便可以将 `my_feature` 和 `target` 作为参数传入（有关详情，请参阅此 [TensorFlow 输入函数教程](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model)），首先，我们会训练 100 步。"]},{"metadata":{"id":"5M-Kt6w8U803","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[],"base_uri":"https://localhost:8080/","height":17},"outputId":"f0368dbe-802d-44fc-9fb8-009f8fa02ae9","executionInfo":{"status":"ok","timestamp":1520096980571,"user_tz":-480,"elapsed":1877,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["_ = linear_regressor.train(\n","    input_fn = lambda:my_input_fn(my_feature, targets),\n","    steps=100\n",")"],"execution_count":21,"outputs":[]},{"metadata":{"id":"7Nwxqxlx2sOv","colab_type":"text"},"cell_type":"markdown","source":[" ### 第 6 步：评估模型"]},{"metadata":{"id":"KoDaF2dlJQG5","colab_type":"text"},"cell_type":"markdown","source":[" 我们基于该训练数据做一次预测，看看我们的模型在训练期间与这些数据的拟合情况。\n","\n","**注意**：训练误差可以衡量您的模型与训练数据的拟合情况，但并**_不能_**衡量模型**_泛化到新数据_**的效果。在后面的练习中，您将探索如何拆分数据以评估模型的泛化能力。\n"]},{"metadata":{"id":"pDIxp6vcU809","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[{"item_id":1}],"base_uri":"https://localhost:8080/","height":51},"outputId":"b725a4fe-e0b3-45f3-a1d5-0ff17da516f0","executionInfo":{"status":"ok","timestamp":1520096988242,"user_tz":-480,"elapsed":5884,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["# Create an input function for predictions.\n","# Note: Since we're making just one prediction for each example, we don't \n","# need to repeat or shuffle the data here.\n","prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n","\n","# Call predict() on the linear_regressor to make predictions.\n","predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n","\n","# Format predictions as a NumPy array, so we can calculate error metrics.\n","predictions = np.array([item['predictions'][0] for item in predictions])\n","\n","# Print Mean Squared Error and Root Mean Squared Error.\n","mean_squared_error = metrics.mean_squared_error(predictions, targets)\n","root_mean_squared_error = math.sqrt(mean_squared_error)\n","print \"Mean Squared Error (on training data): %0.3f\" % mean_squared_error\n","print \"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error"],"execution_count":22,"outputs":[{"output_type":"stream","text":["Mean Squared Error (on training data): 56367.025\n","Root Mean Squared Error (on training data): 237.417\n"],"name":"stdout"}]},{"metadata":{"id":"AKWstXXPzOVz","colab_type":"text","slideshow":{"slide_type":"slide"}},"cell_type":"markdown","source":[" 这是出色的模型吗？您如何判断误差有多大？\n","\n","由于均方误差 (MSE) 很难解读，因此我们经常查看的是均方根误差 (RMSE)。RMSE 的一个很好的特性是，它可以在与原目标相同的规模下解读。\n","\n","我们来比较一下 RMSE 与目标最大值和最小值的差值："]},{"metadata":{"id":"7UwqGbbxP53O","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[{"item_id":1}],"base_uri":"https://localhost:8080/","height":85},"outputId":"78f3c4e1-94ca-4f20-c57d-6290bf9b385a","executionInfo":{"status":"ok","timestamp":1520096994695,"user_tz":-480,"elapsed":1016,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n","max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n","min_max_difference = max_house_value - min_house_value\n","\n","print \"Min. Median House Value: %0.3f\" % min_house_value\n","print \"Max. Median House Value: %0.3f\" % max_house_value\n","print \"Difference between Min. and Max.: %0.3f\" % min_max_difference\n","print \"Root Mean Squared Error: %0.3f\" % root_mean_squared_error"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Min. Median House Value: 14.999\n","Max. Median House Value: 500.001\n","Difference between Min. and Max.: 485.002\n","Root Mean Squared Error: 237.417\n"],"name":"stdout"}]},{"metadata":{"id":"JigJr0C7Pzit","colab_type":"text"},"cell_type":"markdown","source":[" 我们的误差跨越目标值的近一半范围，可以进一步缩小误差吗？\n","\n","这是每个模型开发者都会烦恼的问题。我们来制定一些基本策略，以降低模型误差。\n","\n","首先，我们可以了解一下根据总体摘要统计信息，预测和目标的符合情况。"]},{"metadata":{"id":"941nclxbzqGH","colab_type":"code","slideshow":{"slide_type":"-"},"colab":{"autoexec":{"startup":false,"wait_interval":0},"test":{"output":"ignore","timeout":600},"output_extras":[{"item_id":1}],"base_uri":"https://localhost:8080/","height":297},"cellView":"both","outputId":"829df0b5-6787-4dc8-a9c0-7313dbf0fd06","executionInfo":{"status":"ok","timestamp":1520097064809,"user_tz":-480,"elapsed":1039,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["calibration_data = pd.DataFrame()\n","calibration_data[\"predictions\"] = pd.Series(predictions)\n","calibration_data[\"targets\"] = pd.Series(targets)\n","calibration_data.describe()"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>predictions</th>\n","      <th>targets</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.1</td>\n","      <td>207.3</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.1</td>\n","      <td>116.0</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.0</td>\n","      <td>15.0</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.1</td>\n","      <td>119.4</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.1</td>\n","      <td>180.4</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.2</td>\n","      <td>265.0</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1.9</td>\n","      <td>500.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       predictions  targets\n","count      17000.0  17000.0\n","mean           0.1    207.3\n","std            0.1    116.0\n","min            0.0     15.0\n","25%            0.1    119.4\n","50%            0.1    180.4\n","75%            0.2    265.0\n","max            1.9    500.0"]},"metadata":{"tags":[]},"execution_count":24}]},{"metadata":{"id":"E2-bf8Hq36y8","colab_type":"text","slideshow":{"slide_type":"-"}},"cell_type":"markdown","source":[" 好的，此信息也许有帮助。平均值与模型的 RMSE 相比情况如何？各种分位数呢？\n","\n","我们还可以将数据和学到的线可视化。我们已经知道，单个特征的线性回归可绘制成一条将输入 *x* 映射到输出 *y* 的线。\n","\n","首先，我们将获得均匀分布的随机数据样本，以便绘制可辨的散点图。"]},{"metadata":{"id":"SGRIi3mAU81H","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[],"base_uri":"https://localhost:8080/","height":17},"outputId":"9ee51a41-369a-4c32-f0a6-e57f9a6fb67b","executionInfo":{"status":"ok","timestamp":1520097101480,"user_tz":-480,"elapsed":1054,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["sample = california_housing_dataframe.sample(n=300)"],"execution_count":25,"outputs":[]},{"metadata":{"id":"N-JwuJBKU81J","colab_type":"text"},"cell_type":"markdown","source":[" 然后，我们根据模型的偏差项和特征权重绘制学到的线，并绘制散点图。该线会以红色显示。"]},{"metadata":{"id":"7G12E76-339G","colab_type":"code","slideshow":{"slide_type":"-"},"colab":{"autoexec":{"startup":false,"wait_interval":0},"test":{"output":"ignore","timeout":600},"output_extras":[{"item_id":1}],"base_uri":"https://localhost:8080/","height":361},"cellView":"both","outputId":"0dc95298-11c6-4857-a538-0d650a114497","executionInfo":{"status":"ok","timestamp":1520097115927,"user_tz":-480,"elapsed":1612,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["# Get the min and max total_rooms values.\n","x_0 = sample[\"total_rooms\"].min()\n","x_1 = sample[\"total_rooms\"].max()\n","\n","# Retrieve the final weight and bias generated during training.\n","weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n","bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n","\n","# Get the predicted median_house_values for the min and max total_rooms values.\n","y_0 = weight * x_0 + bias \n","y_1 = weight * x_1 + bias\n","\n","# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n","plt.plot([x_0, x_1], [y_0, y_1], c='r')\n","\n","# Label the graph axes.\n","plt.ylabel(\"median_house_value\")\n","plt.xlabel(\"total_rooms\")\n","\n","# Plot a scatter plot from our data sample.\n","plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n","\n","# Display graph.\n","plt.show()"],"execution_count":26,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAfIAAAFYCAYAAACoFn5YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDBZUjYLRIP58zpUTUTbRvCDMD3/4w77/f+GFFwbdv2TJEixZskTrZlAWC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wtr1qzRsm30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D+dSpU+H1enM2kAPxF+mQenztvPHo7PYPCCDRS8PCwzOhueXBlMwbR2ZyS7GajfAFgigeErFk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\naeRCCCGEhv0JDfrddC/OmHwAAAAASUVORK5CYII=\n","text/plain":["<matplotlib.figure.Figure at 0x7f1cfa973ad0>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"t0lRt4USU81L","colab_type":"text"},"cell_type":"markdown","source":[" 这条初始线看起来与目标相差很大。看看您能否回想起摘要统计信息，并看到其中蕴含的相同信息。\n","\n","综上所述，这些初始健全性检查提示我们也许可以找到更好的线。"]},{"metadata":{"id":"AZWF67uv0HTG","colab_type":"text","slideshow":{"slide_type":"slide"}},"cell_type":"markdown","source":[" ## 调整模型超参数\n","对于本练习，为方便起见，我们已将上述所有代码放入一个函数中。您可以使用不同的参数调用该函数，以了解相应效果。\n","\n","我们会在 10 个等分的时间段内使用此函数，以便观察模型在每个时间段的改善情况。\n","\n","对于每个时间段，我们都会计算训练损失并绘制相应图表。这可以帮助您判断模型收敛的时间，或者模型是否需要更多迭代。\n","\n","此外，我们还会绘制模型随着时间的推移学习的特征权重和偏差项值的曲线图。您还可以通过这种方式查看模型的收敛效果。"]},{"metadata":{"id":"wgSMeD5UU81N","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[],"base_uri":"https://localhost:8080/","height":17},"outputId":"8369f045-2527-4492-e5ed-7a6ccccb9657","executionInfo":{"status":"ok","timestamp":1520097203733,"user_tz":-480,"elapsed":1124,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n","  \"\"\"Trains a linear regression model of one feature.\n","  \n","  Args:\n","    learning_rate: A `float`, the learning rate.\n","    steps: A non-zero `int`, the total number of training steps. A training step\n","      consists of a forward and backward pass using a single batch.\n","    batch_size: A non-zero `int`, the batch size.\n","    input_feature: A `string` specifying a column from `california_housing_dataframe`\n","      to use as input feature.\n","  \"\"\"\n","  \n","  periods = 10\n","  steps_per_period = steps / periods\n","\n","  my_feature = input_feature\n","  my_feature_data = california_housing_dataframe[[my_feature]]\n","  my_label = \"median_house_value\"\n","  targets = california_housing_dataframe[my_label]\n","\n","  # Create feature columns\n","  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n","  \n","  # Create input functions\n","  training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n","  prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n","  \n","  # Create a linear regressor object.\n","  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n","  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n","  linear_regressor = tf.estimator.LinearRegressor(\n","      feature_columns=feature_columns,\n","      optimizer=my_optimizer\n","  )\n","\n","  # Set up to plot the state of our model's line each period.\n","  plt.figure(figsize=(15, 6))\n","  plt.subplot(1, 2, 1)\n","  plt.title(\"Learned Line by Period\")\n","  plt.ylabel(my_label)\n","  plt.xlabel(my_feature)\n","  sample = california_housing_dataframe.sample(n=300)\n","  plt.scatter(sample[my_feature], sample[my_label])\n","  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n","\n","  # Train the model, but do so inside a loop so that we can periodically assess\n","  # loss metrics.\n","  print \"Training model...\"\n","  print \"RMSE (on training data):\"\n","  root_mean_squared_errors = []\n","  for period in range (0, periods):\n","    # Train the model, starting from the prior state.\n","    linear_regressor.train(\n","        input_fn=training_input_fn,\n","        steps=steps_per_period\n","    )\n","    # Take a break and compute predictions.\n","    predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n","    predictions = np.array([item['predictions'][0] for item in predictions])\n","    \n","    # Compute loss.\n","    root_mean_squared_error = math.sqrt(\n","        metrics.mean_squared_error(predictions, targets))\n","    # Occasionally print the current loss.\n","    print \"  period %02d : %0.2f\" % (period, root_mean_squared_error)\n","    # Add the loss metrics from this period to our list.\n","    root_mean_squared_errors.append(root_mean_squared_error)\n","    # Finally, track the weights and biases over time.\n","    # Apply some math to ensure that the data and line are plotted neatly.\n","    y_extents = np.array([0, sample[my_label].max()])\n","    \n","    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n","    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n","\n","    x_extents = (y_extents - bias) / weight\n","    x_extents = np.maximum(np.minimum(x_extents,\n","                                      sample[my_feature].max()),\n","                           sample[my_feature].min())\n","    y_extents = weight * x_extents + bias\n","    plt.plot(x_extents, y_extents, color=colors[period]) \n","  print \"Model training finished.\"\n","\n","  # Output a graph of loss metrics over periods.\n","  plt.subplot(1, 2, 2)\n","  plt.ylabel('RMSE')\n","  plt.xlabel('Periods')\n","  plt.title(\"Root Mean Squared Error vs. Periods\")\n","  plt.tight_layout()\n","  plt.plot(root_mean_squared_errors)\n","\n","  # Output a table with calibration data.\n","  calibration_data = pd.DataFrame()\n","  calibration_data[\"predictions\"] = pd.Series(predictions)\n","  calibration_data[\"targets\"] = pd.Series(targets)\n","  display.display(calibration_data.describe())\n","\n","  print \"Final RMSE (on training data): %0.2f\" % root_mean_squared_error"],"execution_count":27,"outputs":[]},{"metadata":{"id":"kg8A4ArBU81Q","colab_type":"text"},"cell_type":"markdown","source":[" ## 任务 1：使 RMSE 不超过 180\n","\n","调整模型超参数，以降低损失和更符合目标分布。\n","约 5 分钟后，如果您无法让 RMSE 低于 180，请查看解决方案，了解可能的组合。"]},{"metadata":{"id":"UzoZUSdLIolF","colab_type":"code","slideshow":{"slide_type":"slide"},"colab":{"autoexec":{"startup":false,"wait_interval":0},"test":{"output":"ignore","timeout":600},"output_extras":[{"item_id":11},{"item_id":12},{"item_id":13},{"item_id":14}],"base_uri":"https://localhost:8080/","height":975},"cellView":"both","outputId":"57d29521-efc7-465d-dd8e-12742796caae","executionInfo":{"status":"ok","timestamp":1520097531018,"user_tz":-480,"elapsed":56439,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["train_model(\n","    learning_rate=0.00001,\n","    steps=100,\n","    batch_size=1\n",")"],"execution_count":34,"outputs":[{"output_type":"stream","text":["Training model...\n","RMSE (on training data):\n","  period 00 : 236.32\n","  period 01 : 235.11\n","  period 02 : 233.90\n","  period 03 : 232.70\n","  period 04 : 231.50\n","  period 05 : 230.31\n","  period 06 : 229.13\n","  period 07 : 227.96\n","  period 08 : 226.79\n","  period 09 : 225.63\n","Model training finished.\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/plain":["       predictions  targets\n","count      17000.0  17000.0\n","mean          13.2    207.3\n","std           10.9    116.0\n","min            0.0     15.0\n","25%            7.3    119.4\n","50%           10.6    180.4\n","75%           15.8    265.0\n","max          189.7    500.0"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>predictions</th>\n","      <th>targets</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>13.2</td>\n","      <td>207.3</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>10.9</td>\n","      <td>116.0</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.0</td>\n","      <td>15.0</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>7.3</td>\n","      <td>119.4</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>10.6</td>\n","      <td>180.4</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>15.8</td>\n","      <td>265.0</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>189.7</td>\n","      <td>500.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Final RMSE (on training data): 225.63\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABCUAAAGkCAYAAAAG3J9IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZ85EbGw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pkpVXBFu5DB5uDpr3iYiItAkKCkJQUNBD7+/YseOh9/r06YOffvqpMcIyGJFI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\n01ZwcU51n94Y9EzRNlz8+RLP2rlzJx9//DHTpk3jgw8+YOXKl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at 0x7f1cfc2d7810>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"ajVM7rkoYXeL","colab_type":"text"},"cell_type":"markdown","source":[" ### 解决方案\n","\n","点击下方即可查看一种可能的解决方案。"]},{"metadata":{"id":"T3zmldDwYy5c","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[{"item_id":11},{"item_id":12},{"item_id":13},{"item_id":14}],"base_uri":"https://localhost:8080/","height":975},"outputId":"dd5b579d-992e-40ac-be6b-86354400f902","executionInfo":{"status":"ok","timestamp":1520097363060,"user_tz":-480,"elapsed":57755,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["train_model(\n","    learning_rate=0.00002,\n","    steps=500,\n","    batch_size=5\n",")"],"execution_count":31,"outputs":[{"output_type":"stream","text":["Training model...\n","RMSE (on training data):\n","  period 00 : 225.63\n","  period 01 : 214.42\n","  period 02 : 204.04\n","  period 03 : 194.97\n","  period 04 : 187.23\n","  period 05 : 180.80\n","  period 06 : 175.66\n","  period 07 : 171.74\n","  period 08 : 169.21\n","  period 09 : 167.37\n","Model training finished.\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/plain":["       predictions  targets\n","count      17000.0  17000.0\n","mean         116.3    207.3\n","std           95.9    116.0\n","min            0.1     15.0\n","25%           64.3    119.4\n","50%           93.6    180.4\n","75%          138.7    265.0\n","max         1669.2    500.0"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>predictions</th>\n","      <th>targets</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>116.3</td>\n","      <td>207.3</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>95.9</td>\n","      <td>116.0</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.1</td>\n","      <td>15.0</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>64.3</td>\n","      <td>119.4</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>93.6</td>\n","      <td>180.4</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>138.7</td>\n","      <td>265.0</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>1669.2</td>\n","      <td>500.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Final RMSE (on training data): 167.37\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABCUAAAGkCAYAAAAG3J9IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/BHwHBM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4exJ7csv5cnMub6/ayd8mdkcjE5EIIYQQtUhSogmo6UmwJbuQkgobMRFm+qXG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\nZSppSRK3T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wG948QkOEKoqsbBow7W59vI31ZOTe03MZ6doxmdm8y1w5KIb+UxngKBoHkT\nG2Xkp3MG8K+VB9hxpISnF+3g4dn9SQwx6l0gEAiaE0HfXd1333306NFDlGMGga/V2XBOjuoz0ATY\nvKcATcOnz4JH0fwKJIqq8tr7e9m8++yF5/Xrksz4IdlY4y2XbO9LbHF9cIDN+wp9jqnCIYclJjSY\n84fgYlbrojiqOTLvxziPnqDTQ98l5UffDbi9ZCvAuO5NryBxzXTUbkOCOIgbKk6BInvTNeLbec0t\nG0Ct2+sfUe3SkWBR6J3hxBRmnUDTND79opqFq2pwe2BoLwMzRpqxmJpuYly3VePeedmMHm5t8W0M\nmqax50AV764s5MARbztA36vimD0tg949YoUYESEKipys32Ljs3wbRaVek+AUq5EpY73tGVmZrcMc\nVSAQtA5MRj33z+jDwrVHyNt5lj+9vZ1H5vQnM7nhBtkCgUDQlAQtSkRHR/Pkk09GciwtHl8+D5FI\nngjk93Ae2a2St+PshZ/P+yzs/8qGy6P4HZ+vtpC8nefQ63U+/Rnqii23TejO9iPFF4wuL6YhIoEv\ngjl/CN3UUJVdHL3751TvOkDK7Klc9fSjlJZV+92+QYKER/YKEqobopIgNoOGmj6U1+jYX2TBo0pk\nxbvpkuIi0Jy8Ia0MjhqNJZ84OXBCIcoMt02w0L9b060U123VuGFcKrd906rRktE0je17Kln6QSFH\nvomUHNQ3nlnTMujZVbTHRYLqGg+bv6ggL7+MQ8e+jfEcc62VMbnJ9O4hYjwFAkHzRaeTuGNidxLj\nzPx3w1f8ecF2fjKrP12zEpp6aAKBQBAyQd/J9+/fn+PHj9OlS5dIjqdF42tCH4nkiUB+D/VRYKvx\nO75AbRHB+jNEmw2M6NfO59jClXxQ3/knx4fu26EpCscf/C2Vm74gcdIoOv3lt0gBhCTJdg7j2jfB\n5cQ9fAZq18H1H8Rd6xUkNAViUiE6pUGChKbBWbuBY2UmJKB7qky7eI/f7Rsqlh382sOSdTJVNRq9\nOpu4ZZSBxLimMdRyu1VWrAncqtESUVWNz3dWsGxlIV+dqgXg6oEJzJqWSZecln1uzRFF+TbG84ud\ndtyeb2I8e8UxOtfKNYMT23yMp0AgaDlIksS03BwSYky8/fFh/vLOTn4wow8DuqY09dAEAoEgJIIW\nJTZu3Mibb75JUlISBoNB5IzXIdQJfWMN+OaM7YqiqOTtPNfgMdcdX+AY0Pr9Gc6f04zrOl3Yb2PN\nPf29Tr4MRPt1sfpsM6kPTdP4+hdPUv7hp8QNH0TXf/wZyeD/oyGVncO47k1wOfEMn4HadVAQJ+KA\nytNeRSEu01sl0QAUFY6WmiisMmLUe/0jEiyB/SNCFcvcHo2Vm1xs3uNGr4NpI0zcMsFKWVnTpAtc\n3KqREN86WjUUVSP/y3KWfVDIqbNOJAlGDEvilhvSyckWYkS4OXGqhrx8Gxu2fhvjmZVpZkxuMqOG\nW0mxihhPgUDQchnZvx0JMSb+8f4+XnpvD3dO7snI/u2aelgCgUAQNEGLEv/4xz8u+11lZWVYB9OS\nCXZCH64WD71Ox7xJPdGA9T6ECYtJ57OFItD4AseA+m+98HdOj39vGI4aV4OEl/pep3AaiJ75898p\nWfQ+0X170v3N59BZ/LeYSGVnMa57yytI5N6E2mVg/Qdw2qHyLCB5DS3N8Q0ap+yR2FdopkrWE2dW\n6J0hYzH4yzv55jn1iGXTcnOolT0XXr9zJQr/WS1TZFNJT5K4fbKFrFR9kwgArbFVw+PR2PC5jfc+\nKORckYxOB6NzrdxyQwbthW9BWCm3u9mwxcb6fBtfn/FWocTF6rl+XCpjcq10yYkWHh0CgaDV0L9r\nCj+fO5C/Ld3Dm6sOUeGQmZabI77nBAJBiyDou/usrCyOHTtGeXk5AC6XiyeeeIJVq1ZFbHAtiWAn\n9A1t8fBXMXD7hO7odRKb9hQgu70ihMWkIyUhijMl/v0QfI0vUFtEoNaLSLStBLvPxhqIFrzyNgUv\nv4Wlcwd6LHwRfZz//n2vIPEmuGQ8196M2nlA/QeoKQNHkdfIMiEbTA0zobI7dewvNONSdKTHuume\n6kIfhIYVSCwrq3Tyh/lfUuGQSYozk5WSQ0FJAooK1/YzMm2ECaPhyt/MtMZWDbdbJW+zjeUfFVJU\n6sKglxg/Mpmbr88gM004pocL2aXyxc4K1ufb2LXv2xjPqwclMCY3mUH9vo3xFAgEgtZGl3YJ/OqO\nQTz/7m7e33iCiiqZOyb2aNGVhQKBoG0QtCjxxBNPsHnzZkpLS+nQoQOnT5/m7rvvjuTYWhTBTOgb\n4tkQTMWAJEkXBAkAp0vlTEk12Wmx1Djd9RpC9utivXDcOWO7Eh1lYvPuc0G1XoTDh+JK7NMXJYve\n5/QTL2LKTKfH4lcwplj9biuVnvFWSHiCFCQ0DaqLvaKEzgAJHcDYsJXwgkoDR0pMaECXZJn2CZ6g\nrSjqMwUtd8hIkhGXK4czRQkY9ArfvzGGq3KaphqhbqvGffOyGZ1rbbErPbJLZd2GUv67qoiycjdG\ng8SUsancNCWd1GTRMhAONE3j4NFq8vLLyP/y2xjPbp28MZ4jhiURH9dyq2sEAoEgFDKTY/j1vMG8\n8O5u1u86h73axX039sYUhvsmgUAgiBRB36nt3buXVatWMW/ePBYsWMC+fftYu3ZtJMfW4vDlc3Dx\nhL4hng31VQwEmsDXOD08dtdQ3lp1iB1HS/2Oe/yQ7Av/1+t03DOjL1OGZQfVFtFYH4ortc+62D76\nlBOP/hlDUgI9Fv8dc/sMv9t6BYk3wePCk3sLauf+gXeuaVBVAM4K0JsgsYP33xBRNTheZuKs3YhB\np9Er3Yk1OriWnPPUZwpq1CcRbeqETjLg8pQj6c/ROSuIFJEwc1mrxvhUbpvRcls1ap0Kq9eX8r+P\ni6io9GA26bhxYhrTJ6djTTQ29fBaBYXFMuvzy1i/xUZRiTfGMznJG+M5ariV7HZRTTxCgUAgaBoS\nY8384vZB/H35XnYeLeUvi3fx45n9iI0Sf38EAkHzJOg7fpPJO6lyu91omkafPn14+umnIzawlkh9\nPgehejYEEhy2Hyq54AcQaAJfK3u4e+pVHHg5H6dLuWyb5HgL1vjLV/CDbYtoqA/Fld7nxdg3fsHx\n+3+DLspC94UvEtWtk99tpZLTGD95yytIXDsTtVO/wDvXVLCfBVcVGCxeQUIX+sTarcD+IgsVtXqi\njSp9Mp1EGwP7R/ijrlgWH2OiwuEh2tQRsyEVTVOodp3A5Smh1k1YRJ9gaW2tGtU1Cqs+LWHFmiKq\nHApRFh233JDOtAlpJMSLm8HGUl2jkL+tnLzNZRw8+m2M5+hcK2NyrfTuGYdelCkLBAIBUWYDD8/u\nz78/PMjnB4p48j/beWT2AJIThH+RQCBofgQ9W+rUqRMLFy5kyJAhfPe736VTp05UVVUFfM4zzzzD\n9u3b8Xg83HffffTt25dHH30URVFITU3l2WefxWQysWLFCt566y10Oh2zZ89m1qxZjT6xpsTfhD5U\nz4aSilr/goND5vfzv2Bgt5R6J/Bmo54R/TIjEtPZUB+KK73P8zh27efo3T9D06DTv54hdkBvv9t6\nzp34RpBw4xkxCzWnb+CdqwrYT4O7BozRXg8JXehjdcgS+wotOD06UmI89EyTaUwbfF2xrNSu498r\nqgEzHrWaavk4quYEwiP6BMuufZX8a+FpClpBq0aVw8MH64r5cF0J1TUKMdF6bp2eyfXjUomLbZnV\nHs0FRdHYtb+S9fk2vthZgcvtjfHse1UcY76J8YyyiLJkgUAgqItBr+Oeab1IiDGx5svT/GnBNh6Z\nPYD2af79swQCgaApCPpu+fHHH8dutxMfH8+HH35IWVkZ9913n9/tt27dytGjR1myZAnl5eXcdNNN\nDB8+nLlz5zJlyhSee+45li1bxowZM3j55ZdZtmwZRqORmTNnMmHCBBITE8Nygs2N+lo84FsfiR2H\niwm0Nl7hcJG38xzZabE+RYmLJ/DBHDeS59Qc9uk4fJy9sx9AV1PL2il3YD+oMFA54jP5RCo+RU3e\n298IEjPrFyQUN1ScAkX2pmvEt/OaW4ZIiUPPwWIzqibRMclFTpI7aP+I+jDodew6YmDtFy7ARK37\nHE73WbjoXdZY0ScYWlOrRkWlmxWri1n1aQlOWSU+1sAdt7RjythUoqPERLkxfH26hrzN3hjPivMx\nnhlmxlybzMhrrMKTQyAQCIJAJ0ncOq4bibFm3s07xpMLd/DjW/rSo0PDoskFAoEgEkiapgWsCT9w\n4AC9evViy5YtPh8fPny4z98rioIsy0RHR6MoCrm5ucTExPDxxx9jMpnYuXMn8+fPZ+7cubz33nv8\n5S9/AeCxxx5j9OjRjB071u+YSkoCV2g0hNTUuIjs1x/+0jQAFq074tcDwBfWODP9u6Ww51jZZRP4\nupPtQMc9T0Nfi2D2Hep+gPDs80wh2yZ/B6PNxvpxMznUe9iFx8YPaX9JoodUfBLjJ28jKR7c181C\n7dgn8M49sleQUN0QlQSxGYSqJGgafF1u5GS5CZ2kcVWaTGrs5e02DaXMrrJojZOvC1QSYyXmjDex\n7cgJn6KPr2jacHw+WkurRmpqHIePlPH+x8Ws/qwEl0sjKcHA9MnpTBqdgsXcNsSISHxnVtjdbPjc\nRt5mG1+f9sZ4xsboue5qK6NzrXTr1DxjPK/034+mJjU1rqmH0Cgida3a2vugOSKuQWC27i/k3x8e\nRJLgnmm9GdozLezHENeg6RHXoOkR18A3ge4f6l2afP/99+nVqxevvPLKZY9JkuRXlNDr9URHeycb\ny5YtY+TIkWzatOmCN0VycjIlJSWUlpZitX6bemC1Wikp8e2j0Jrw1+IRyEfCHxUOmUlDs5k9pmu9\nE/jGRmgGorH7ri9ppKG4S20cuvV+jDYbW669/hJBAi5N9DgvSKB4iLrhTuSkzvXsvNYrSGgKxKRC\ndErIgoRHhYNFZspqDFgMKn0ynMSaG+YfURdN09h+yMPy9TKyGwZ0M3DLGDPRFonuHfz7n4Sbnd+k\nahQUycTF6vne3GzGX5fSLCeYgSgulXlraSEfrC3A49FIsRq5aUoG40cmYzKKqMmG4HKrfLnTTl5+\nGTv3VaKqoNfDsIHeGM/B/eIxitc2ZMIlEgsEgtbDNb0ziIsx8ffle/nn+/uwj+92idm5QCAQNBX1\nihK//vWvAViwYEGDDrBu3TqWLVvG/PnzmThx4oXf+yvQqKdwA4CkpGgMhvDfZDWH1Z+C0mpsVYEj\nPOuSkhhFl5xkLCYD7cM0jiv5WjhdHsorZd7f9JXPpJHoKBP3zKinfcIP7koHW+98GPmrU+wcPJrd\ng0dftk15lRO9yUiSs5CaT98G1UPU1DsxdutPaoB9uxx27KUnQVOJzcwhypoe8viqajU2H9GoqoW0\nBBjeTY/JEJ5ez+palTdW2Plin4zFLHHfLfHk9o+6TAgI9j3TkPdEYbGTl14/zmdbSpEkSEz3QJyd\nTw7WUK2v5u5pvdHrm/+E88y5WhYsO8XHnxahKBrtMizMm9mByWPT2/SEuaHfE5qmsfdgJR9/WsSn\nm4pxVHurgnp2jWPy2HTGj0wjMaFlGYM2h78fAIqiMn/lfrbuK6CkopbUxCiu6ZPZYj5rF3PkyBHu\nv/9+7rrrLu644w6OHz/OY489hiRJ5OTk8Ic//AGDwdDqfKkEgkjSO8fKL+cO4vmlu1m07igVDhe3\njOrc4hYJBAJB66JeUWLevHkBv6jefvttv49t3LiRf/7zn7z++uvExcURHR2N0+nEYrFQVFREWloa\naWlplJZ+G1dZXFzMgAEDAo6pvLymvmGHTHMps1HcCtY438aV/ujXJZkqey2hjt7fStqVei0urowo\nq5TxZ5q/efc5pgzLDnm1T3XKHL7jx1Tt3I/11ukc6zIGqlyXbZcUZ4EzR6nesAgUD56Rc5ATO5NK\ngDJfpx0qzwISxLfHoUTjCPE1s9XoOVBkxqNKtE9w0znZhb08pF345dhpD4vWytgdGjmZOuZOtJCc\noFBa6mjQ/kJ9T7jdKv9bXczSDwpwuTSSU3W4oiuQzN5I0+LyWlZs/IqaWtclrTPNjdPnannvwyI2\nbrWhal5Pg+/e1okBvaLQ6yUqKqqbeoiNojGr6Q35nigqkVm/xcb6fBuFxd7vuOQkIxNGpjAm10p2\nljfG0+1yUlLiDGnfTUlz+fsBl7f/ReKzdiUEmJqaGv74xz9eUo35l7/8hXvvvZdRo0bx8ssvs2rV\nKsaNG9emfKkEgnDQMSOO38wbzHNLdvHR1pNUOGTumtITQwsTLgUCQeuhXlHi/vvvB7wVD5Ikcc01\n16CqKvn5+URF+c+Br6qq4plnnuHNN9+8cHOQm5vL6tWrmT59OmvWrOG6666jf//+/Pa3v6WyshK9\nXs+OHTsuVGe0RcxGPf26ppC342zA7STAGt8w88dItUmEypJPj11y86z6KZIpr3KGHFOpeTwc+8Gv\nqMrfTtINY+ny7K8ZmHfcp1fHlI4eoj9bCJqKZ9StqNlXBd55TRk4irxGlgnZYIoJelzg9Y84bTfw\nVZkJSYKeqTIZ8Z6Q9uEPj6Lx8VYX67d7DTInX2Ni7BDjFY1JvLhVIyHewPfnZrJ67xFsVerl217U\nOtOcOHGqhmUfFLJlewWaBh3bW5g1NZNrhiSSkR7fbCagDeVKfgfU1Crkf1lOXr6NA0e8opjZpGPU\ncG+MZ5+rRIxnuAjU/tdcP2v+MJlMvPbaa7z22msXfnfy5En69fPGMl933XUsWrSIlJQU+vbtS1yc\nVygZNGgQO3bsCOhLJRAIIDUxil/NG8zflu4hf18hldUu7r+pDxZTyzOdFggELZ96v3nOr1L8+9//\n5vXXX7/w+4kTJ/LDH/7Q7/M++ugjysvLeeihhy787qmnnuK3v/0tS5YsoV27dsyYMQOj0chPf/pT\nvve97yFJEg888MCFm4u2yvjB7QOKEomxJh6Z3Z/UpGjMRj2yW6HMXnPJamdAI821R8jbee7Cz+fb\nJIArtmodindGqDGVmqpy4mdPULFmA/EjhtHl708g6fU+Ez2mdPQw2b7eK0iMnBNYkNA0qC6BmlJv\n1GdCRzCGlvetqHC4xEyxw4BJr9InQybecvlkvSEU2VQWrnZytkQlJUFi7iQLHTPqn4CEq/e8pMzF\n/MVn2FonVaNadrE430+0bQMEp0hy9EQ1S1cW8uUuOwBdOkYza1oGQwckoGtFE+e6gmC4vwMURWP3\nAW+M5+c7vDGeAH16xjImN5nhgxOJEukkYcfukP3HSDezz1p9GAwGDIZLb1G6d+/OZ599xowZM9i4\ncSOlpaUN8qWKVAsoNJ82nraMuAbBkwo886PreHrBNrYdLOK5pXv4/feuITGucdHg4ho0PeIaec0d\nFAAAIABJREFUND3iGoRG0HJoYWEhJ06coFOnTgCcOnWK06dP+91+zpw5zJkz57Lfv/HGG5f9bvLk\nyUyePDnYoTQ7wm0oZo23YI0zYfPRagAwsHsq7dPiUFSVReuOXLLaOaBbChqw+2jpZSugAIvWHeWz\nXed87vf8StqVINDNc11CianUNI3Tf/wbpe9+QMyAXnSb/yw6s9dcVa/TMXf8t+aO1uqzRG9455sK\nidtQ2/cItGOoKgBnBeiNkNgR9KFFEjo9EvsKzThkPfFmhd4ZMmZD4w0tNU0jf6+HFRtlPAoM62Vg\nxkgzZlPgSXS4Vsvrtmpc1S2Ge27/NlXDYJSwxvtuSQpVcIoUB486WLqykJ37KgHo0SWGWdMyGNQ3\nvtX12UZyNf3kmVry8svYsKWccrsbgMx0M2NyrYwabiUtpemvdWsmIdbc7D9rjeEXv/gFf/jDH1i+\nfDnDhg3z6UEVjC9VJFpAoXm18bRVxDVoGPdOvQqLQcemvQX89IXPeGRO/wYLmOIaND3iGjQ94hr4\nplHpG+d56KGHuOuuu5BlGZ1Oh06na9NtFhCZEmhFVXnvs+PYq30LErFRBuaO7wb4Xu38ZPulFRYX\nr4ACASswzq+khcssMxCBbp51EmiANS709pSCl96g8NWFWLp1ovt/XkQfe3lrhdmoJ91ZgHHDItC0\nIAQJFexnwVUFBgskdgBdaOWNFbU69hdZcCsSGXFuuqe6/HpohEJVjcqSdTIHv1aItsDtkyz06xrc\n2MKxWn5xq0ZivIEffieLUcOtl0zkzUY9A7un+mydCUVwCjeaprH3kIOlKwvYd8jbVtCnZyyzpmXS\nt2dsqxMjzhPu1fSKSjcbt5azPr+Mr059G+M5eUwKo3OT6d65ecZ4tkaa62ctXGRmZvLqq68CXs+q\n4uLiBvlSCQSCSzHodXz3+p4kxpn4IP8kf16wnYdm9ycnI76phyYQCNoIQc+sxo8fz/jx46moqEDT\nNJKSkiI5rhZBJEqg6+6zLmajHo+i4VFCiw7dcbik3qTKK7mSFujmedSAdkwa1iHkypPiBe9x5qlX\nMGVl0POdv2O0+jY6k84dw7h+IWgapYNvxpzeFb9nrSpgPw3uGjBGez0kdKHd2J+rNHC0xIQGdE2R\nyYr3hJoa6pODX3tYvFbGUavRPVvPrRPMJMQGJ4Y1drW8bqvG1PGp3DqjHTHRvp/jq3WmIX4o4UDT\nNHbsrWTZB4UcOuY1qhzYJ56ZUzPo1T08ySfNmXCsprvcKp9uKmHFx2fYsffbGM+hAxIYk2tlSP+E\nNp1K0pQ0p89auHnxxRfp168fo0ePZvny5UyfPl34UgkEYUKSJG4e2YXEWDML1xzh6YU7eeCmPvTp\nnNzUQxMIBG2AoEWJs2fP8vTTT1NeXs6CBQtYunQpQ4cOJScnJ4LDa75EogQ6GJ+F8ioZu8M7mQi2\n/QHAViVT3zz4Sq+kBbp5DrXSpGzFWr7+5VMYkpPosfhlTO18x3NK545hzFuIoqq8VjOQze+XYo3f\n6rPCRXG7oPxrUGQwx0F8ltfcMkhUDY6VmjhXacSg0+id4SQpqvH+ES63xspNLvL3utHrYPp1JkYM\nMKILQelo6Gp5fa0a/qjbOhOuVqdQUFWNL3fZWbqykOMnveXbQwckMGtaBt06hWZW2pJp6Gq6pmkc\nPl5NXr6NzV+UU13jjfHs0jGa0blWRlydRGJ8y4rxbI00h89aONi3bx9PP/00Z8+exWAwsHr1an72\ns5/xxz/+kZdeeokhQ4YwevRoAOFLJRCEkbGD2pMQY+LVFQf427I9fPf6nuT2yWzqYQkEglZO0KLE\n7373O26//fYLnhA5OTn87ne/Y8GCBREbXHMmEoZiwfgsnF/JdLkVEmJNVDh8t3nURQJMRh2y2/ek\nePTAzLCspIXirxGum+eK9Vv46ke/QxcTTY+FLxHVpaPP7aRzRzHmLUJRVf5S2oe9srcs8XyFS43T\nw7xJPbxj8MhUnDjuFSSikiA2g1DKG1we2F9kwe7UE2NS6JMhE2VsvH/EmWKFRaudFJVrZCTruH2S\nmXYpob9mDVktD6ZVoz7MRv0VN9pTVI2t2ypY+kEBJ884kSTIHZLIzKkZ9YoprZVQVtOLS2XW53tj\nPAu+ifG0JhqZPqUdw/rH0rG9/xQmQdPRFJ+1cNKnTx+f9xfLli277Hct3ZdKIGhuDO6Rxs9uNfHi\nsj28/sFB7A4Xk6/uIFrxBAJBxAhalHC73YwbN44333wTgKFDh0ZqTC2CKLPBryjQkDYIRVVZ/cUp\nJMnrqeiP/t2See+z4+w8UhK0IAFejwZ/ggSAQa9vVBRgY/w1GnPz7Ni+l2Pf+znodHR/6zli+vX0\nuZ3u7BEM698BCf5VM4i98uUrafn7Cjl8qpyx/VOY3B1UTYGYVIhOCUmQqJJ17Cs0I3t0pMZ46Jkm\n09job1XT+GyHm1VbXCgqXDfAyA25JoyGht0ghLJaXlLm4oXX9vPZltKgWjWaC4qisfFzG8s+LORs\ngYxOglHDrdxyfTrZWW17Il2fIFhTq7BlWwV5+WXsP+z12zCZJEZek8SY3GT69oprFdGoAoFAIPBN\n9+xEfnXHIJ57dzdL1x+n3CFz67huIVVlCgQCQbCE5NZXWVl5QSU9evQoshx8+0Br4eLJtz9RoCFt\nEEs+PXZJTGddLCY9I/plommaX8+J5HgLA7ol41FVNu4qQA1hYb6xrvuRjhj0Rc2hYxye9xNUl5tu\nrz9D/PDBPrfzChKLQJIoGTKT/OVFfveZHqsxJscNqo7Ydjk4lNDEkqIqPYdLzKgadLK66JDobrR/\nRHmVyuK1MsfOKMRFS9w6wUzPjo3PEa9vtdxXq8a9d2STk928V1/dHpX1+Tbe+7CQohIXej2MG5HM\nLTekk5keWoRra+diQVBRNfYeqCIvv4ytOypwubxfIL17fBPjOSSRaBHjKRAIBG2GrNRYfjNvMM+/\nu5t1285gd7j4/tReGA3CM0ggEISXoGc2DzzwALNnz6akpIRp06ZRXl7Os88+G8mxNUsCGVEmx/su\nga6vpSGQl4QEXN07nTsmdkev0/Hb17b63C4x1sRjdw0hLtpEcXkNG3YWhHRejcmwr6pxsf1QZCIG\n/SGfOsvh2x5Eqagk5c+/JnrsCJ/b6c4cxvDZOyDpcI+5HUtKDtb4Cp9tC0M7WbhnZAIasGBrNQ98\nJxnstZce18+11DT4ymbkdIUJvaTRJ0MmJUZp9HnuPuph6adOamXo3VnP7LEWYqPDs0oRaLV8x147\nry86c6FV49EHujKoT1SzLt10uVXWbSjjv6sKKbW5MRgkJo9J4aYp6SKKMgCnztaSt7mMDVvLsVV8\nE+OZZmbMtSLGUyAQCNo61ngLv7xjEC8t28OXh4qpqnHx4M39iLY0fnFEIBAIzhP0N0qnTp246aab\ncLvdHDp0iFGjRrF9+3aGDx8eyfE1KwKJB0mx5guiwHmCbWmoz0tixohORJuNFJfX+N2uwuFiyafH\n+O71PeuN2/RVQXFxy4nT5eFMiQM0jdSkaL+Cwvnz23ao2G/VSGPEDn+4iks5NOcB3EWl7JpwE58X\nJmJ97XKzSt3pQxg2LP5GkLgDLbMzZvDZtjD2qmjmXhOH7NZ4aV0FR4pczK2UL3xAAl1LVdNxsNiM\nrcZAlFGlT4aTGFPj/COcssZ/N8hsO+jBZIBZY81c3dsQEVHg4tXy4lKZ+YvP8PkO+yWtGjkdE5tt\nqb5TVli9vpT/fVxEud2DySQxbUIaMyanYU0y1b+DNoi90s3Gz8vJyy/jq5Ne4S0mWs+k0SmMzrXS\no0tMsxagBAKBQHDliLEY+emtA/jXigNsP1LCUwt38PDs/iTFCdFaIBCEh6BFiXvuuYfevXuTnp5O\n167eSgCPxxOxgTVHAokH9mqZWtlziSgRbEtDIBHBGv+tWJAQa8Zs0uN0+V6Bz99XSLTFwNzx3f36\nBWSlxnK62HHZ7wd2T8Ggl1i49jD5+4qolb3X1mLSkds3k9vGdbvMG6K++FIIf8yox17Fkbk/Rj55\nhu1Dx/LlVV5RrO5rqzt9EMOGJV5BYuw8tIxOF/ZxvpJlx+ESbFUyNw2KZdqAWOw1Cs+vKeeUzUNy\nvIWkeDNV31RK+LuWBqOZjp16UuvWYY3ycFW6TGOLQk4UeM0sbZUa2Wk6bp9kITUpsqWSbrfK+x8X\nsezDwhbTqlFTq7Dq0xJWrC6m0uHBYtZx05R0bpyUJlIgfOB2q2zbbScv38aOvXYUBXQ6GNI/njHX\nJjOkfwImEeMpEAgEAh8YDXp+OOP/2TvPwKjOM21f00ejaRp1BAIkkACJIppBNAkjMAZsbGOwsR07\nTlknm3xbsptt2ay92Th1vS3ZFLdkHds4LsEU22CqDaJ3BKoUgVAftZE09Zzvx6BRmxkVkEV5rz82\nc86ceWd0pjz3eZ77zuTNHcXsPl7Bi28c5a/WTmNEzN2TXiUQCIaOfosSVquVH/3oR0O5lluegSQW\nDCQydGARfeGvwHccO5RfwJqcFN7bcyGoj8A7u0rZeayi2/GcboldxypQKhTdhJT+xJcGX//g8bU5\nKX76L2k7V8y5yXM4MmdZr31OFNexLtWNNv89vyBx71PI8WO77RMYW1iYwsXiYibEQU2zl3/f1kBt\niy+wbr1WTUuY55qUGEdU/HjaPUpGWd2k2G7MP8Inyew47ObTIx6Q4d6ZGpbdo0WlGtor1j1HNQaT\nqvFF4mj1snVHLVt21OBo9WGIUPHoqgRW5sVhNop20q7IskzxhTb25Nez73ADjlb/+Z2SHEHOvGgW\niBhPgUAgEPQTpVLBk3lpRBl1fPDZBX70h2P8xaNTGZdkGe6lCQSC25x+/4LPy8tj06ZNZGVloVJ1\nFpkjRowYkoXdiug0KqaMi2H38Ype23oW3wONDO1PRF+Tw4XTHTpBo+exQ/kFBLu9L5HheFEtq7LH\n0O7yYjHq+hw5iTLqmDEh9qbEjAJIHi+lz/09jsOnKB0/lc8XrQ6aiJHiuULE/nOgUvs7JOLHBD+g\nLKFrq2RCHNjb4Vd7W6l3+IL6ggR7rpkTxpGVOQGfJJEU2Uxq9I0JL3WNEm9td3K5SiLKpODxpXpS\nk4bWVLDbqIby1k/VaGr2sPnTGj7aWUu7U8JkVLH+oUTuvzfull3zcFFT52LvAX+M57Vq/7kbZVHz\n4H1x5GZHixhPgUAgEAwKhULByuwxWIxafv9xET9/+wR/9mAGWeNjh3tpAoHgNqbfokRRURGbN2/G\narUGblMoFOzZs2co1nXL0eEpcKrEX7h3eDNEd/EW6MpAuiqg74i+jmNGhzhmqGOHitvseXtfIoO9\nxcXzrx2h0eH3U5iSGh3y+VmNWp5/dla3UZYbQZYkLv7VCzTt2EdVygR2LV2HHCRmdJa+hm9H9UOQ\nkHzQdAU8baAxYIsZxd9/ibCve8dzVatUZM+ayphRSbS2tXP81CkWrJs0+Ocmyxw572XjXhcuD2Sl\nq3kkR0eEbui6FG63UQ17o4dN26r5ZHcdLreE1axm7QOJLMuJIUIvxIgO2tt9HDjmj/E8W3g9xlOj\nYME9UeTOi2bKRNOQd90IBAKB4O5gwZQRWCK1/O/Gs/zigzN8aVk6i6YlDfeyBALBbUq/RYlTp05x\n5MgRtNq70ziup6dAh1nklNTooJGXAxvJ6H6/UKaQ4Y7Zn2OHI5yI0kGDw7+tvtnF7hPXGBVnDLr/\nzAlxN0+QkGXKv//v1H/wMbppGWyd+ziSqvdpO1tfw7ds55CUKnz3Po0cNzr4AX0eaCoHrwt0JjAn\ngUKJTkmfr/uBc/XkzpuFzWqhuraevQeOMn9y3KDHU9qcMu/ucnK61IdeC08s0zE9fWhb6Y+faeKV\nN69SWXN9VOPpJBbNuTVHNersbv70cTWf7q3D45WJjtLw1JoRLFkYg04rvA/geozn+Rb25Ns5eKwR\n1/VOqklpRnKzbWTPihIxngKBQCAYEqakxvDdx6fzn++e4vefFNHocPOV1ZOHe1kCgeA2pN+iRGZm\nJi6X664UJcKNNpwus+Py+IIWpv0ZyRgoPU0aOzo2rEYtWeP7d+xgsZb9ETx60truIXd6EqdL67C3\nuLCZgneN3AjXXnqZ6tfeIWJCKim//w/M757vJYR0CBI+pRop70soQgkSXhc0loPkgYgoMCYEHQEJ\nxrK56cQna1CpNBSXXaK0rJT5k+MG/VyLr3jZsN1FU6tMyggljy/VYzMPXaF9O41qVNW4+OCjKnbv\nt+P1ycTFaHnk/gRy59nQCCNGAK5UtLM7385nB+3UN/hjPBPidORk28iZayM+VjiiCwQCgWDoSRlh\n5h+fmsFL75zkw30XaWz1sDYnhQid8HgSCAT9p9+fGNXV1SxevJjU1NRunhJvvvnmkCzsVmKg/hAd\n9GckY6B0Paa92cn2I1c4XVpPo8PF6bJ6VKrSXpGjHfQVUbpu8ThkWe6WvqFVK3F7g/tYNLS4cLt9\nyLKMLPu7Gm4m1a+9Q8W//xb1yETG/t9/ERlr6yWczImo5ptR5/EpVbDsGRSxycEP5mn3CxKyDyJj\nwRDTL0FCluFas5qSOi1qFYyJamfSrEgsubMH9bf0emU+OuBm7wkPSiUsn6tl8QwNSuXQdCrcTqMa\nFZVO3ttaxWcH7UgSJMbrWLMigYVzbKjVt14nxxdNc4uXzw/5fSJKL7UBYIhQsXSRP8ZzwjgR4ykQ\nCASCL54Em4F/emoG//PBGfaeuErhZTvfeDCD5HjTcC9NIBDcJvRblHjuueeGch23NP3xhwjWfdBB\nuJEMCN650Bc6jYrdJyrYe/Ja4LZQkaMd9BVRqlIqeSIvnefWTON8aS3IMhE6NS++cTwwutFtDVoV\n+89WBf5tb3GHffyBUPv+R1z+3s9wRpr40+KnUG8qIyutmTU5KYC/+yTde5lvWM/hVWqQlj4dWpBw\nO6DpKsiSvzvCYOvXGnySTFGtlqoWDRqlTEaCE2uEBAyuoK+q9/HmNhfX6iRirAqeWKYnOX7oOhVu\nZFRjMOfkYLl8tZ33tlSx/0gDsgyjkvQ8uiKB7NlRqIZIrLld8Hgkjp5uYk++nWOnO2M8Z0wxk5sd\nzcxpFjHKIhAIBIJhx2LU8fdPTOfjI1f5055S/u3/jvH4kvHkTBshBHOBQNAn/RYlZs+ePZTruKUJ\nN9owbXw07+8tC9l9EI6+OhfCMZDIUZfHR21jO8eLavq1v16rJjHaEFhbMEEiHD2PF2zt4Qrexp37\nuPCXL+DW6tn04FdpssZADwFl3ehWIg6eB40O7n0aReyo4ItxNkFzBaAA80jQm/v1HFxeBXvPydQ7\nNBi1PjITXOg1g+sEkWWZ/ac9bN7nxuuDOZlqHligQ6cZmi/pmjoXr719lUMn/KMaq/LiWPdgYr9G\nNUKdk99am3XT11l2qY13N1dy6EQT4I+pXLMqgXuyrEPWOXI7IMsyJRfa2N0jxnNscgQ52TYW3GMj\nyiJiPAUCgUBwa6FWKXl2VQbJMQZe3XqeN7YVUXi5gafvm4BBL8Y5BAJBaMQnRD8J5Q8hyTI7w3Qf\nhGPDzhJ2HuuMF+24ryzLPJGXHva+/RkpibbouxWYoUrqniMoTreX331U2K0LoivRZj3pyVYOhNje\n83gdIoTRoGXj5xfCijAth05S8tW/w6dQ8vEDX8Yek9jt2PtOV7I22YHh8EbQ6PAseRo5ZmRgezfB\nw9MEjipQKMEyCrSRIV/PrjQ7lZyt0uH2QZzRS3qsC9UgL0Y3t0q8s8NF4WUfBj08dZ+ezNShedu5\nPRIfdhnVmJRm5OtPjhpQ/GOobhpDhJbV88bclHUWljp4b0sVx043A5CWYuDRVYnMmGK+q6+m1Nnd\n7Mm3sye/nooq/3vbalbz4LI4crJtt+TIjeDWQZZlrlW5KChyUFDcQlFZK3kLY3hkRcJwL00gENxl\nTB0Xw/NfnsVvNhVwpLCGS1XNPPdgJmMT+3dhSCAQ3H0IUaKfBPOHAPjeyweD7t+fboH9Z4IX9XtO\nXOOhhSkYdKGvhlqMOnRaFU63r9c2rUaFxajrVWCGomMEJRB7WlpHbaMz6L5Wo5bvPzMTrUZFUXlD\n2JGWnlfddVolTnenP0VPAaetoJjip/8S2edl+/1fomrE2F7HnqmqIOJQIWh1eJY8gxztj5/q+ViP\nzbGQNykCWaFCYU0GTf8K86oWNUW1WmQZpiQriFK7+uuF2YuCC17+uNOFo10mPVnFY3k6zJFD02p/\nM1I1wnXfHDxbyfLZowY9yiHLMgVFDt7dXMXp8y2APyFi7aoEpkwy3bViRLvTH+O5J9/O2cIWZNkf\n4zl/dhQ52TamZZhFjKcgKJIkc+Wa0y9CFLVwrthBY7M3sN1sUhNlFR01AoFgeLCZ9Xx3fRYbP7/I\nRwcu8+Ibx1i7eBxLZoy8a7/zBQJBaIQoMUC6+kPUNLQNygAToLaxPaigAH4vgz9sL+brqzL6WE3o\ncQJ3mAKzJx0xom/tKO5TxGhuddPu8mIyaPuMPO15vK6CRFdOFNexcrSWsvXfxtfSSvJ/vkBrrQV6\nvLYLDJV83VqIU1Yj5XwJTXRnHnaHAKNQwFPZZnImRFDT7OXgNRUPLOxbkJBkuFCv5WqTBpVSZlKC\ni/QRBmr79xJ2w+2R2bTPxYEzXtQqWL1Qy7ypGpRD8CV8I6MaPQnXfVPX2B72fA6FLMucKmjhj5sr\nOV/SCsDUDBOPrkwgI/3uNMDySTIFhS3s3m/nQI8Yz5xsG9kzo27JVBTB8CJJMpevtnO2iwjR4uj8\nDomyaJg/O4qMdCMZ6UZGJurFD3+BQDCsqJRKHlmUSnqylZc3n+PtHSUUXm7g2RUTidQL0VQgEHQi\nRIkboD8GmCHpI6mi8FJDyKhR8BeQoYp8l9vH1RpHyAITQIFfxe6IKA13lbwrXZ9XuMjT/h4PwFVZ\nTen6F/HW1jP63/6W+EfvZ8KWc93GRxYaKvmatZA2Wc2P66fxZ9po4jruf/2x1Cr4+iIrM8foKa/3\n8NL2BjQaLcvmhn4dATw+OFetp6FdhUEjkZngxKAdnH/ElRofb25zUtsgkxit5IllOhJjbn6BeTNG\nNXoS7nyOsUaEP597IMsyR0818e7mKkou+pMiZk41s2ZlIump/RujudO4Wulk9/569h7ojPGMj9WS\nmx3Nwrk2EuNEjKegE59P5mJ52/VxDAfnih20tnWKEDE2DdPnWgIiRGKcTogQAoHgliRzbDQvPDub\n324q4ERJHc+/doTnHswgNcky3EsTCAS3CEKUGCQ+SeL9vWW0Oj1Bt3d0C4TCYtSFjdtsanOHvDLt\nkyS2HS5HqfBf4e+JzaxnZJwxZIEZbdbxF2umEBtlCKyxvil010dXpoyLDtwnXORpf4+nc7axatNr\neGurSPrO14l/dh0Aj+elcay4BqdbCggSrbKaH9VN44rXxLbD5azP8yeGNDlctLe7+eulNiYkajlf\n6eIXOxpp98goneE7VlrdCs5U6nF6lUQbvEyMd6EexISFJMnsOe7h44NuJAkWTtNwf7YWzRBEWR47\n3cSrb/lHNaIsar759EgWzom64YIknKHrnMzEfo1uSJLMweONvLu5iktX2v33nWHl0ZUJpIy++zwR\nmh1e9h1qYHd+PaUXO2I8lSxZGE1udjQTx4sYT4Efr1em9FIrBUV+AeJ8iYN2Z+f3Q3yslnumW8lI\nN5KZbiQuRohYAoHg9sFq1PE3j2WxOf8Sm/Zd5MdvHueRRaksnT1qSDpJBQLB7YUQJQZJKL8GvVbF\n/CmJgS6CnnT1PgglSADYwnRavLOrlN0nrgXdBn5BJPx4RSwj47q3zoe7St6VJTNGdvt3qCSN/hxP\n7XGzfNPrWGoriX92HSP++muBbQadmvlTRuA5d5ivWosCgsRlj3/du09cQ6XyiyIWg4p/XBXNCKua\no5ec/HZvI97rFxTDdazUtqoorNbhkxUkW92MtXkG5R/R0CLx9nYnZRUS5kgFj+XpSE+++W+tXqMa\nS+N47MFEDBE3rxMjVPfLs6sysNtbQ97P55PZd7iB97ZUcbXSiVIBC+6JYs3KBJKTBt+9cTvi8Uoc\nP93M7v31HDvdjNcno1TA9MlmcufZmDXNKmI8BXg8EiUX2ygoaqGgyEFhaWtglAdgRLyO+bONZKSb\nyEg3EmPTDuNqBQKB4MZRKhU8OH8saaOs/HZTAX/cXUpheQNfWTERk0F8xgkEdzNClBgE4UYTDDo1\njyxKDRnp2V/zyay0GMDvW9G14A/32EoFLMpKChSWnQVmLfYWFzZTZ9pFT8JdJe8g2qzHZtYDfceZ\n9nU8pc/Lym1/IKHqMraH7iP5X7/T64rx+uRGdJVFtPg0vFg3jXKvsdv2E8V1PDJ/FDrHVUZY1ewu\nbOMPB5q7TcYE61iRZbjcoOFSgxalQmZSvJM4Y3B/j744UezhvV0unG6YnKpizWI9xoibq/gHRjW2\nVOH23JxRjVCE6n5RhYgf8Xpl9hyo54Ot1VTWuFAqYfE8Gw+vSCApQX/T13erIssypZfa2L3fzr7D\n9sCs/5iREeTMs7FwjojxvNtxuSWKy1r9IkSxg+KyVtyezg+rUSP0gVGMSWkmbMKkUiAQ3KFMHB3F\nC8/O5uUt5zhdVs/zrx/hzx7IIG2UdbiXJhAIhgkhSgyCcIaAjQ5XyHGBcIJCRz1uM+mZOj4aWZb5\n3ssHexX84R5blmHZrFG9BBFZlpFl/3/D0SFW7D9TSburd5HetcAPFR0JnVGoqxeksO/0tV7eFwpJ\nYtmud0m4UIhlyXxS/vN5FD3WrCw+gubQZnzaCH54JZMrPQQJAJPWi6b5MiAhRURT6VZhM7mD+lt0\nFNgqlYrCGh11rWp0aonJCS6MutAdK6FwumQ+2OviWKEXrRrW3qtj9iT1TW/FP3a6iVfeukpVx6jG\n2pszqtEXXQ1dg+HxSOzcV88HH1VTW+9GrVKwNCeGh5fHEx9797SV19nd7D1gZ0++navuQaitAAAg\nAElEQVSV/sQai1nNqqVx5GbbGJt8942sCPw4XT4KS1sD6RglF9vwev2fwQoFjB4Z0SlCjDdiMQsR\nQiAQ3D2YI7X81dqpfHzwMn/67CI/fesEqxeM5f65o8U4h0BwFyJEiUEwWIPLcIICMvzNY9NISbLw\n/t6ykAX/I4tSQz62zdz9sTfsLGHnsYrAv+0tbnYcvYosyzyRl97r/h1Xyb+6ejL/s+EEheUNNLS4\nuhX4EF5c6RqF6mhzd2tH9j9Pmfl7NzL6/Al006cw7tc/Rqnpfhoqiw+jObQZWWegPfdp2t692CuJ\nY0Kilv+3JAoFEhgTUBpsrF8SzyOLOgUItUrRrZtjZLyV+XNmotGqsep9TEpwoh3E5MPFaz7e2u7E\n3iwzKl7JE8v0xFpvbjv+FzGqMRhcLontn9Wx8eNq7I0etBoFK5bEsvq++Lumvbyt3cee/Hr25Ns5\nfd4f46lRK5g3y0pOdjRZmSLG826krd3H+RJHwJiy7FIrvuvarlIBY5MNARFi4ngjJqP4+hUIBHc3\nSoWCFXPHMH6kld9sKuCDzy5QVN7AV1dlYIm8O35TCAQCP+JX0SAIN5oQzuAynJhhM+tJue5C3FfB\nPyU1OqinRNfHdnl87D9T1WsfgP1nqliTMy7kOiMjtHxl5aSQfhHhxJWuUajBnu+sg9vIOHOQxvgk\n5v/uJVSG7i3+yqLDaA5vRtZF4sn7MpqoeLLSHN1e65ljdHxtkRWlQoHCPBL05sC2rlf4u0aSJsbF\nkD1nBhqtFkdTDQtTIlEOsG70+WS2H3az86jf3HTJLA1LZ2tvagH6RY5qDIS2Ni9/+riKD7fV0NTs\nRa9T8uB9cTy4LP6uGEuQJJmzRQ725Ndz8FhjwIBwwrhIcrOjmTfbSqRBfJzeTThavZwr9osQRRfa\nKClzBIyHlUoYN8YQ8IOYMM4oYl4FAoEgBGmjrDz/5Vm8uvW8f5zjtcN8fdUkJo6xDffSBALBF4T4\nFT1I1i0ehyTL5J+pwun2Xw7Ta1XIsoxPkoJ6SvRHzKhpCJ1a0dDi5I1tRRSVNwAE0jdsJh3T02O7\ndTIUlTcE1tUTp9tHbWM7I2N7j0T0XG+wFv7+dor0fL5TTnzGjCO7aLJE0/BP/4QhpvvsoLLoEJrD\nW5D1fkFCtsYD3c0XpyYpWD/HjE9SgHUU6LsbdnbQtZtj4vgUZkydhCxJ5B85SYO9lrzJ9/QrTaKD\nukaJN7c5Ka+WsJkVPL5UT8qIm1tkDNeoRjha27xs3VHL1p21NLd4MUQoWbMygVV5cZhNd/7HR0Wl\nk935/hjPOrtfjEqM07NqqZWcuTYS4+8e34y7neYWLwXFLdfHMRxcvtoe8K9RqxWkj4tkUpqRzHQT\n6eMiidALEUIgEAj6i8mg5f+tmcL2w1d4f28ZP99wklXzxvDAvLEoB3oVSSAQ3Hbc+VXFEKFSKlEq\nFN0Kf6fbx85jFSgUiqAxmRA63aDj9nAFv1ajIv9sZ/dDx1W5qeNjWL8kDZ8k8daOYk4U1/aZokEf\n/hLhCCeudI0Mhc7na3/vI2Z/voV2k4WG7/8zax6e2e1+ysKDaI5sRdYbrwsScYFtKqWS9feOZ+1s\nC2pnPZJCRYsmjkiVgVA/+5scLhodHubNmkbqmFG0tTvZk3+UOnsDSgVhY0K7Issyh8952fiZC7cH\nZqSreShHR4Tu5n1B1tS5ePXtqxy+hUY1mlu8bP60ho921tDWLmE2qXl8dSIrlsTe8R0BLQ4v+w43\nsCe/nuIL/hjPCL2SJQuiycm2sTA7kfp6xzCvUjDUNDR5AskYBcUOrlQ4A9s0aoV/FCPNn44x7554\nmpvbhnG1AoFAcPujVCi4755kxo+08OsPC9i0/xLFVxr52qoMokx3j1+VQHA3cmdXF0NIOF+Ffacr\nQ6ZS9Ew3iNCpaXd58fpkVMq+UjCCCwmnS+tx5fp6eVGEQq9VEduPgjwcXZM96ptdga6NUyW1qJSK\nbs93ua+Skq1vozSbmPrer7BmpnU7lur8AdRHP/ILEku/jGyJ6/5gsgwtVaidDThc8ItddkoqK3q9\ntt2eo17P/ffOJ8pqoc7ewO79R2l3+ouKcL4fXWlpk/j9R07OlPnQa+HJ+3Rkpd28UYVbcVSjocnD\nh9uq2ba7DqdLwmJW86WVCTy5Ziytre3Dtq6hxuOVOH6mmT35do6ebArEeGZlmsnNtjE7y4pO5z/H\nxBWbO5M6uztgSllQ5OBadaewq9MqmTrJdN0TwsT4sQY0ms7PHJ1OdEUIBALBzSI1ycLzz87ita3n\nOVFSx/OvH+ZrKyeRmRI93EsTCARDhBAlBkk4XwWn2xfooAiWSgGgVinYcexqUPEiWDfFhGQr+88G\n94hoaHFS29geUiTpSfbkhAGNLgSjQ1zx+SR2n7gW6NroMNME//NtPnCM0uf+AaVWw4Q3/xtjL0Ei\nH/XRj5EjjHjynkW2xHZ/IFmC5gpwtdDQDv+6sYamdv88f6jXtsmppKA6giirkrJLVzhw7DSS1Gm4\nGc73o4Oici/v7qyloUUiNUnJ40v1RJlunpnlrTaqUWd3s/GTaj7dW4fbI2Ozalj/8AiWLoxBp1Ni\nMKhpbR2WpQ0ZsixTdqmNPfl2Pj/UQLPDC0Bykp7cedEsnGMTsYx3MDV1Ls5eH8UoKGqhutYd2KbX\nKcnKNAeMKVPHGNCob66ZrUAgEAhCE6nX8K2HJ7Pz2FX+uLuUl/54ivvnjOahhWODjkgLBILbGyFK\nDJJwYxbB6JpKAaEjNX2SzFNL07t1U1iMOnySxLHi2qA+EVEmPchy6GSP6ygVsGjaCB6/d/wAnmlo\nXB4fp8vqg247UVzHcpuHsqf/GiSJ8a+/hHHG5G77qM7loz72MZLeSPU964k02OjWvyD5oOkKeNqQ\n1BG8tO1aQJDo+Vgdr21ls5riWi0ykGJzcu1iHVFGbdBRmWB4vDIf5bv57KQHlRJWZGvJma65aVfH\nb7VRjepaFx98XM2uffV4vTKx0Voevj+exfOj0WruzC/9+obOGM8r1/zdM2aTmlV5ceRk2xibHDGs\nPh6Cm48sy1TVuAJ+EAXFDmrrO0WISIOKWdMsZKQZmZRuJCXZIBJUBAKBYJhRKBQsmTmKcSMt/Grj\nWT46eJniq40890AGNrPwdBII7iSEKDFIwo9Z9Mbe4uRCRVOfCRt7T1SALLM+L61HkkRZSOPKrLQY\nYqMMfYokMrBsdvINKcxdEznCdYtI5Vco+9/vI7W2kfqrF7HkzOm2XXVuP+pjn9CqjOCluiyK3izB\nZi7vHMeQJWgqB68TdCbqfFFcs18M+lgNLU4aW1w0+6xUNGtQK2UmxTuxGaRe4k64DonKeh9vfuKi\nsl4iNkrBt9ZFY9Q6Q+4/ENweiY0fV/P+1pszqhEqGaW/XKt28v6WKvYcsCNJkBCn45EV8eTMjUat\nvvOKMafLx8Hjjf4Yz3P+GE+1WsHcmVZyr8d43onP+25FlmWuVjoDIsS5Ygf2Rk9gu8mo4p7pFjLS\nTWSmG0keGYFKjOUIBALBLcmYBDP/8sxsfv9JIUcKa/iX1w7zlZWTmDYuZriXJhAIbhJClLgBeo5Z\nWI062lzeoOKBAvjZhpNEm3WkJ0eFLuZl2H3iGiqVMjCSEM6/Qq9VsXpBSr9EEls/vRSC4ZMk3tlV\nyvGiGuwtbmwmLVPHxQQVQiJbGlm58RWk5kbG/OQfiH4gr9t2VcE+1Me30ao08M/XJlPt85+GHd0i\nRq3MA5OUIHlAbwVTIhavFFJ0ibOZqGiLotmlxqCRmJzoJELT6b8RKkWkA0mW2XfKw9b9brw+mDtZ\nzar5OkaO0FBbe+OiRM9RjT9fN5IF9wxuVKPj7xDKs6QvyivaeW9LFfsPNyDJMDJRz5qVCcyfHXXH\nXRmWJJmC6zGe+Ucbcbo6Yzxzsm3MmxWFMVJ8BN4JSJLMlWtOCopaOHtdhGhq9ga2W8xqsmdaAxGd\no0bohTeIQCAQ3EYY9GqeezCDiaOjeGtHCf/93mmWzhrFmpxU1Ko7s7NTILibEL/Ib4CeppUWoy6k\n2WSH50J9s4v8s1XotaqQnQ/QfSQhXEeC2+PD0ebGoFMHRJJ9pyuDHrs/XgqheHtnCbuOVQT+bW9x\ns/vENUbGRgKda9O3t7Ji4ytENjcw8u+/SdxTj3Q7jqrgc9THtyNFmPn32qkBQaKDZJua3DEekJRg\niIHIWFAoQoouURYzixfModmlJibSy4Q4FwMZ/W5uldjwqYuich+RevjScj0ZKTfnbdFzVOOBpXGs\nu8FRjVBjP9DdV6MnFy638e6WKg4eawRgzKgIHl2VwJzp1juuOKuocrIn387eA/ZAi35stJZVS23k\nZNsYIWI8b3t8kszlK+0BP4hzJQ5aHJ2fedFRGhbOiWLS9XSMpASdGMkRCASC2xyFQkFOVhIpI8z8\n6sMCth+5QsnVJp57MINY6/CZhAsEghtHiBI3ga5X4rt2T9hbnCjoFCQGQkOLMxBbGc6/omuSRIdI\nsnpBCm9/WkxheQMNLa5+eSmEw+XxkX+mMui22oZ2RsZGcq2uFZXLyf2bXsPWUEPs1x4n8dtf7rav\n6uxnqE98imywUHXP4xT/oajb9gkJWr69xIpOo6BFacNk7J7C0bMzZeK4ZLKmZKJUqhgT5WZ0lIeB\n1B1ny7z8caeTVidMGK1i3RId5sgbV9uHalQjQqcO2THT07Okg+KyVt7dUsnRU80AjBtrYO2qBGZO\ntdxRRVqLw8v+Iw3szrdTXOZ35NTrlCyeH03uPBuTxhvvOPHlbsLnkym73BYQIc6XtNLW3ilCxEZr\nmTHFEkjHSIjV3lHnt0AgEAg6SY438S/PzOSNbUUcKKjm+deP8Oz9E5iRHtf3nQUCwS2JECVuIh3F\n4yOLUnlkUSoXKpr42YaTwfd1+5gzKY7D52uCihZdxYZwoxk9ux9c1zsnnlyWDnBDvgMd1Da04XT3\nNpgEcHklrta2ovR6Wbb1/4irvkLhxJmcnnkfT3mlwOOqzuxFfXIHcqQFd96zGPUWbOZLAaFlxhgd\nX19kRQG8eaiNtcsn9HqsDtHl4YWplNaqqHNGolLITIh3EhsZuuuk15o9Mps+c3GwwItaBQ8t0jJv\niuamFDFDOaphMWppdLiD7ttVxAIoKGrh3S1VnCpoAWDi+EjWrkpkaobpjinWvF6ZE2eb2L3fzpFT\nTXi9/hjPaRkmcudFc0+XGE/B7YXHK1F2qS3gCXG+xBEYvwG/B8rcGdZAOkZcjMivFwgEgrsJvVbN\nV1dOYsLoKN7cXswv/3SWe6ePZO3icSItSSC4DRGixE0g1Jz/6gUpRIfocLCZ9Ty9fCIROjW7T1zr\ntb2n2BAsJrRr98ONeg2EpY8iViFJ3LvtbUZeKeViSgZ7730EuaCaoiuNZKXF8kRsBerTu64LEl8B\nUxQ6CAgtOekRPJltxu2V+cXORkaMiAsponh9UFJvoN6pRq/2+0dEavvfinKl2seb25zUNsokxih5\ncpmOhOgbT774IkY1QgkS4BexzJFaThU088fNVZwrdgAwZaKJR1clkJFuvCPECFmWuVDezu799f4Y\nzxa/b8CoEXpy59lYOMdGdJR2mFcpGChuj0TJhdaACFFU1oqrixCalKAL+EFkpBvF31ggEAgEKBQK\nFkwZQUqif5xj5/GrlFY08dzqDOLDeIkJBIJbDyFK3ATCzfn31eGwPi8NlUoZUmyA3h0YwbofBus1\n0B9irRGhPTBkmYW73ie17AwVSSnsuG89slIVWIOp6HO0lZeQI624854FU1TgrutyU5kc72NyvExz\nu4/X9rcxYkRcyDGTNreCs1V62jxKoiJ8TIp30t8GEEmS2XXMw7ZDbiQJFmVpuH+u9oYTF3qOamSk\nG/naE4Mf1eggnLlpT2QZEiKt/MvPygKjC9Mnm3l0VQITxhlvaB23CvYGN3sPNrA7v54rFddjPI1q\nViyJJXdeNCkixvO2wuWSKLrQSkFRCwVFDorLWvF4O8XF5CR9QISYlGYkyqIZxtUKBAKB4FYmKdbI\nPz89kzc/LWbf6UpeeP0IzyyfwOyJ8cO9NIFA0E+EKHGDhCseTxTX8cJXZgX+P5jo0NMsM0Knpt3l\nxeuTgf51P/S1hmBeAwNBp1Exb3ICO7sYXXYw79AnTDx3hNrYJD5Z+Qw+dWfx8LDpIo+YL1EvRaDN\nfRptF0ECWUbVVsPkeBlZqcFjSuIbjxpDrrO+TcW5ah0+ScFIi4eUaDf9tQiwN0u8vd3JhWsS5kgF\nj+fpSEu+8VP/6KkmXn375oxq9CScuSlAlFFHo8OF1mfAadezr8RfqN+TZeHRVYmkjrn9rxC4XBKH\nTvhjPE8VNCN1xHjOsJKTbWP6ZIuI8bxNaHf6KCztFCFKL7Zd/4zzN2KNGRVBxnVTyklpRswm8dUk\nEAgEgv6j06h49v6JTEyO4v+2FfHrDws4f7mBx+8dj/YGfgMLBIIvBvHL7wZweXxcqGgKOp4BYG92\ncrmyJWyHQwdqlYIdx652EyAMeg1XahyBfUJ1P4QrYHt6DQyWx+4dj0Kh8K+vxYXNpCO35CAxh3fT\naI1h64NfwaPrSDWQecR0iYfNl6jx6nmxfhrfwUDAfkiWoLkCXC2g1qGwJBOtCn4lVJbhSqOGC3YN\nCgVMiHORYPIG3TcYx4s8vL/bhdMNU1JVrFmsJzLixgrZ6lr/qMaRkzdvVKMn4cxNbSY9yyaPY+Mn\ntVyrcqFQSMyfHcUjK+IZM+r2FiMkSeZciYM9++3kH22g3elv4U9LjST3eoynySg+tm51Wtt8nC9x\nBESIssttSNenMZQKSBltCIxiTBxvFNGsAoFAILgpzM1MYEyiiV9tLGDvyWuUVTTxjdWZJEZHDvfS\nBAJBGMQvwUHQ079BqQiesKFQwM83nOyXv0Ow8YtQYkfP7of+pnPcCD07OrxbtnH1nbfRJMbR+Lff\nw1Av42xx4RckLvKw+TLVXj0/rMuCSGvnGiQfNF0BTxtoDGAZBcrgxbxPgqJaHTUONVqVRGaCC7M+\nuOFmT9pdMu/vcXGiyItOA+uW6Jg1UX1DXQxDNaoRjGDmprIM7mYN1ZUR/O+xqyiVkJNt45EVCYxM\nvL1jLq9Vd8Z41tR1xniuWOKP8UxKuL2f351Oi8PLuRJHIB3jUnl74DNRpYLxYyOvx3P6RYibKeAJ\nBAKBQNCVxOhIvvelGWzYVcqeExX86++O8tSyNLIzE4d7aQKBIARClBgEPQUEOYTPYseP8r78HQbi\nHwC9ux8Gks5xo+g0KtQHDnLpH36EOsrChA2/JGv8WB70+PjDJ4WMKD/A6uuCxL/VZWH36VnSsQbJ\nC43l4HWC1gSWJFAEF2mcHgVnq3Q43CrMOh8ZCS506v4ZWl6o8PHWdicNLTLJ8UqeWKYnxnpjZp/d\nRzU0/Pm6pJs2qhGKjhGf44V1VFVIuBr0eN1K1CpYsjCah+9PIDHu9k0dcLT6Yzz35NspLO0S4znP\nRk52NBnpIsbzVqWx2UNBcS0HjtRSUOTgckV74HNQrVYwYbzx+jiGkfRxkeh1QoQQCAQCwReHVqPi\nS8vSmZBs5XcfF/LKlvOcv9zAk3np6LTiO0kguNUQosQACScgdHRMhOqcCOXv0Jd/QE+CdT/0lc4x\nGDoMNruOnDR9fpiyb/4Tygg9sb/6KcoxyQDo1Eq+llSBpvEytZKBH9ZNRRFpZUnHGnxuaLwMPg/o\nrWBKDJnq0diupKBaj8enIMHkIS22f/4RPp/MtkNudh3zAJA3W0PeLC0q1eAL2y9iVCMUXi9YsNJw\n0UVrgweNWsHyxTE8tDye2OjbM33AH+PZzJ78eo6cbMLjlVEoYGqGiZxsG3OmW0UBewtib/QERjEK\nihxcrXQGtmk1CjLSjWReN6YcnxKJTivi2AQCgUAw/MyeGM+YBBO/+rCA/WequFjZwjcezCAp9s4w\nAhcI7hSEKDFAwgkIMvDVFRN5dev5oNtD+TtE6NRYjNqwkY9dCdb90HO8IpR3RX/w+STe2lHcy2Bz\npc1NybN/gyTJfLb6GQrzm7CdPUjW+BietF1Cc24fkikafe7T/K2k61yDxwlNl/2jG4YYiIwNKUhU\nNKkprfMX3ONjXIwwe/tKJAWgtkHizW1OrtRI2MwK1i/TMzZx8MWt2yPx+tuXeOPd8iEf1ehJu9PH\ntj11fPhJNY3NXnRaJQ8sjePB++KxWW+/FAJZlrlY3s6efDufHbLT1Oz3BBmZ2BnjGWO7PUWWO5U6\nu5uzXUSIyurOzzy9TsnUDBOzs6IZM1LL+LEGNBohQggEAoHg1iQuysA/PjmDd/f4O51/8PujrM9L\nY8GURJHcJRDcIghRYoD0ZUA4OTW63/4OXb0p+itI6LVKJFnGJ0lB/Sl0GtUNm1q+trmgl7/F0e3H\nGLvxNyjb2tm+/EkuRo+5vs1JTMlnaEzlSOZoPHnPojWYO00t3a3IjVcACW9EHBpjTNDHlGQoqdNS\n2axBo5SZlOAkKqJv/whZljlU4OXDz1y4vTBzgpqHFunQ6wb/JXP0VBOvvHWF6lr3FzaqAX5zwI93\n1bJpezUtDh8ReiWPrIhnVV4cFvPtJ0bYGz18dtDOnvx6Ll/1X1k3GVWsuDeWnGwbqWMM4sfALYAs\ny9TUuQN+EAVFDqrrOj+PDBFKZkwxX/eEMJE62oBarSA21kRtbcswrlwgEAgEgv6hUfsv3k1IjuK1\nref53ceFFJY38NTSdCJ0ohwSCIYb8S4cIH35N5gM2pDbJyRbu/27pzdFf3C6JXYdq0CpUAT1pwhH\nsHGMYPscPFvZ7TZjcwMr//QKqlYHR1Y8xsXUyde3yDxuLmOl6QrVUiSG3KfRGsyB+/naG6HpGjIy\nr+5torS+gaw0ey/DT7cXCqr1NDlVRGp9ZCa4iND07R/haJf5404nBRd8ROjgqSU6pqUNvnjvOaqx\nbvVIHsiLHvJRjRaHly07ati6o5bWNh+RBhWPPZjI/ffG3nZJEy63xOHjjezuGuOpUjAnEONpRqMW\nV9WHE1mWuVbtCogQ54od1Nk9ge3GSBWzplkCIxljRkXc0AiUQCAQCAS3CtPTYkmON/KbDws4WFAd\nGOdIjjcN99IEgrua26viuUXoy7+h53Z/PrLM/rNVFJY3kJUWy+oFYwdkbtmTUP4UweiZFhIuDaTJ\n4aK2sT3wb32bg5UbX8bY2sTB+fdzMnX69S0yT5jLuN90hWseAy/WT+XvfLrODon2BpTNlbh8Er/Y\n2ci5a/4rrz0NP1tcSs5W6XB5lcRGepkQ50LVj5q18LKXDZ+6aGmTSU1S8fhSHVGmwRW7bo/Enz6u\n5oMeqRozs+KG9EpwY7OHTdtq+HhXLU6XhNmo5slHRrB8cextlU4gSTLnSxzsyffHeLa1+ztcxo81\nkDsvmnmzozDfZuLKnYQsy1y95qSg2BEYx2ho6hQhzEY1c2ZYA8aUo0dGCINRgUAgENyxxFgi+Lsn\npvPBZxf45FA5//Z/x3j83nHkZCWJDk6BYJgQlUIQ+uoo6Mu/oev2N7YVkX+2KrCtI4mjzekdkLll\nT0L5UwQjWNxoqDQQi1FHrDWCmoZ2NC4nKz58FWtjHSdm5HAlZxk2Wcbe4uIJSyn3G69S4THww7pp\nqI0W/2iKLENbHbTW0uqWeOkTO5fqvd0eo0NQaXRqKarVIckw1uYm2erp0z/C45XZut/N56c8qJSw\ncp6WRdM1KAf5JdJzVONb65KYP4SjGi6Pj8sVDvbsb2Ln5/W43TJRFjWPrU5kWU7MbWXyWFnjYk9+\nPXvz7YF2/xibhuWLY8nJjr7tY0pvVyRJpryiPSBAFBQ7aG7pfA9GWdTMnx0ViOgcNUIvfoQJBAKB\n4K5CrVKyNncc6aOsvLr1PG9sL+Z8eSPP3DcBg16URwLBF41413VhIB0F0D//hqLyhqC3F15uCOk9\n0R+6+lOEE1HCpYUcL6rt1W2h06iYk5nI1t1FLN/yO2JrKzifMZtD2ctZkhYLskxC6R6WG69y1WPg\nh3VZNEtaf+ynWgmOKmhvwIeKFzfXUtXs6/W4jS1OimvU1Dv1qJQyk+NdREf23q8nlXU+/rDNRVW9\nRFyUgieW6RkZN7givueoxoPL4lj7wNClavgkidc2F7Mvv5nmOhXICiIMCr6yJomli2LR3iZGga1t\nXvYfbmR3fn23GM+cbBu586LJFDGeXzg+SeZSeTsFxX4/iHPFDhytne+n6CgNC+dEkXE9HWNEvE6I\nEAKBQCAQAFPHxfD8l2fxm00FHC2s4VJlM0/kpTF1XHAPNIFAMDQIUaILA+ko6A/hkjoaHS7mZiSw\nv0sXRQej4oy0Ob2B0Q+nu3fBnpUWg1qlCJqS0VVECbcGe4uLP2wr4pn7J3QTXZ5Zno75xR9hrLjA\nhXGTOfvgepZMiGNdbiqaYx+jMV6l0mfkR/VT0RjN/tjP3BRorgBXM6h0eI0j8VAHdF+7VqNh8byZ\n1DsNRGgkMhOcRGrD+0dIssznJz1s3e/GJ0H2ZA2r5mvRagZeWLncEhs/ruaDj/yjGpkTjDy9Lgmz\nWYFqiN4NldVOfvpyMZcueAA1So0Pvc2F1uymmUi0mviheeCbhM8nc+BoPRs/usrhE50xnlMmmsid\nZ+Oe6VYi9LdPh8ftjtcrc+FyW0CEOF/iCIzMAMTFaP2eEGl+ESI+VitECIFAIBAIQmAz6/nu+iw+\n3HeRjw6U81/vnWZqajSPLxl/w+bxAoGgfwhR4jpOtzdkR8FA/Bu6Ei6pw2rUoVIrUCnB1yVkIsEW\nwd8+PhWNWk2Tw4XRoGHj5xeD+lf0R0QJtwaA/WeriNCrA/vLksS5b3wf44ljGOfPJvd/XuQhmwmd\nWon6yFZURYeQLHEYc7/E33s1/u4MFdB0BTxtoDGAZRQ6ZW9DUIvJSO68WZhNRmwGLxPjXPT1kjY5\nJDZ86qL4ig9jhIJ1S3RMGju407bnqMY3147gqqOO32w92a/OmIFy5Vo772+t5kUpM64AACAASURB\nVLODdmQZlBoJfbQTralzTGWw59YXwcXyNnbn2/n8oJ3G6zGeSYk6crOjWTRXxHh+UXi8EqUX2wLG\nlIWlrThdnR8aiXE6smf6RzEy0k3ERou/i+DOoLi4mG9+85s888wzPPnkkxw5coSXXnoJtVqNwWDg\npz/9KRaLhVdeeYVPPvkEhULBt771LRYtWjTcSxcIBLcZKqWShxemMntiPG9uL+ZUWT0FlxpYfk8y\n988dfUv+ThMI7iSEKHGdhubQHQUD8W/oSrikjsgIDZ+drOx1e5W9ne/+6iDzpyQGiuNg/hXhxjK6\nFrrh1tBzf61aSfm//ifVb/yJyKwM0l//OapIA8gy6sNbUBUfRrLG48n7Mlp9pN/UUvJCYzl4naA1\ngSUJFP6Cvqvhp8FoYf4909Go1Yy0uEiN9vbpH3GmzMsfdzppc8LEMSrWLdFhMgxcLAg1qrFxfxk7\nj1UE9rvRzpgOLpa38d6WKg4ca0SW/YV8I3Y0xt6eGYM9t4aKhqbrMZ777Vy66jc8NUaqeHjFCOZM\nNzFOxHgOOW6PRPGF1oAnRFGZA7e7s5toZKLeL0Bc94SwRQkRQnDn0dbWxg9+8APmzp0buO1HP/oR\nP//5z0lJSeHXv/4177zzDsuXL+ejjz5iw4YNOBwO1q9fz/z581GpRAEhEAgGzshYI99dn8Xh8zW8\ns6uEzfmXyD9bxeNLxpM1Pkb8BhIIhgghSlwnyhy6o6Crf8NAWb1gLG1OL4WXG2h0uIgy6ZmSauNU\nWX3I+zjdvkBx3FWM6Fq4hhvL6Fro+iQJWZbRqZW4vFLY/b1vbKD6t29hnJjK+Df+67ogIaE+vNUv\nSETF41nyZdBH+u/oc0PjZfB5QG8FUyJdq26VUsnj96Zxz7SJXGnSoVRAeqyTeFN4/wiXW2bjZy4O\nn/OiVsHDOTqyJ6sH/EUQbFTja0+MIjkpol+izkApudjKu5urOHKyCYDU0QYeXZXAlAwj33/1EPXN\nve9zI+fWzcLlljhyspHd++2cLGhGkvwxnvdkWcjJjmbGVDMjEi1DmkRyN+N0+SgqbQ2YUhZfaMXr\n7RQhRo/UB/wgJqUZsZoHH3srENwuaLVaXn75ZV5++eXAbVFRUTQ2NgLQ1NRESkoKhw4dYsGCBWi1\nWmw2G0lJSZSWlpKenj5cSxcIBLc5CoWCeybFM3VcNJv3X2L7kSv84oMzZI61sT4vjQTbrXEhSSC4\nkxCixHX0WnXIjoKstJgBtW25PD7szU52HLvK6dK6wGjA3IwEHs9Lw9HmZs+Ja30eZ9/pSo4X1dDQ\n4u41WhBuLKNrofvOrtJu3QDBiDLp8WzcSsWP/xdtUgKzP3oNhy7SL0gc2oKq5AhSVAKeJc90ChIe\nJzSV+zslDDEQGUvPNgCfBIU1Ompb1ejUEpkJLky64MJIB+VVPt7c5qSuSSYpVskTy/TE2wbeHXHk\nZBOvvt0lVeOxJObP7kzV6I+oM7Kfj3W+xMG7m6s4cdavOqSnRvLoqgSmTzYHHu9mnVs3C1mWOV/S\nyu78evKPdMZ4jhtrIDfbxvzZNsymO/vjoa+UnaGivd3H+dLOeM7SS634rut0CgWMHRURECEmphlF\nnKrgrkStVqNWdz/3//Ef/5Enn3wSs9mMxWLhO9/5Dq+88go2my2wj81mo7a2NqwoERVlQK0emvd8\nbKxpSI4r6D/ibzD83El/g2+ujWLVonH8duMZThbX8v1XD7F60TjWLkkjQnfrfj/fSX+D2xXxNxgY\nt+67aRjoOm7Q07+hP3RN7+gpFtQ3uwL+DY8sSu1X8obT7QuYXPYcLdBpVEwdH8OuIILD1PHRfY54\ndGVBQykVP/wf1NFRpG/4JREjE3DUNKE+uBlV6VG/IJH3ZdBdV4bdrX4PCVkCYwIYbL2O2e5RcLZK\nR6tbhUXvIyPeiTbM2SZJMjuPeth+yI0sQ+4MDffN0aJWDaw7oqrGxWsb/KMaKpV/VGPdA4lE9EjV\n6K+oEwpZljlT6ODdzZWcLXQAkDnByKOrEpk8wdirq+NGz62bRVWNi70H7OzOr6e61h/jGR2l4b7c\nWHLm2hiVFPGFrmc4GGjKzo3S2ublXHFrwJjywuU2pOvanFLp76iZlG4kI83EpLRIIg3iY1kgCMYP\nfvADfvGLXzBjxgx+8pOf8NZbb/XaR5bDGycDNDS0DcXyiI01iY6yYUb8DYafO/FvoFfCtx/K5Hhx\nLRt2lvDerhJ2Hiln3eJxzJoQd8uNdNyJf4PbDfE3CE44oUb8+u1CKP+G/tLTeDIYHaMBffk89HV/\nnUZFqI/AjtvDdQMAWI1aFrorSfyv/0URGUHsr3+KMnkksiyhPrgJVekxJFuiv0OiQ5BwNvtTNpDB\nnAR6S6/jNrQrKajS45UUjDB7GBfjJlxKZH2TxFvbnVyqlLAYFazP0zFu1MBOzY5Rjfe3VuHxdh/V\nCEY4r41w3QuyLHP8TDPvbakKRGJmZZpZszKBSWnGkOu70XPrRmht85F/tIE9+XbOFfsFFJ1WSc5c\nGznZNjInmlDdRTGeNztlpyfNDi/nrkdzFhS1cPFKOx11klqlIC0lMmBKOSE1spdgJhAIglNUVMSM\nGTMAyM7OZvPmzcyZM4eLFy8G9qmuriYuLm64ligQCO5QFAoFM9LjyEyJZuuBy3xy6DK//rCAvSev\nsT4vjaSYyOFeokBwWzOkokRP5+zKykq++93v4vP5iI2N5Wc/+xlarZZNmzbx+9//HqVSydq1a3n0\n0UeHcll9otOoBmw82N+uhI7RgHWLxyHLMvvPVAWN/Ozr/hajjpMldUH3OVlSz5ocX9huAHOkhu9k\n6qj8yn/hRcHeh75C8ecNRJ86wF+MuEhKSzGSbQSeJU93ChLtDdBS6e8xNyeDrnsRLstQ0aymtE6L\nAkiLdTHC7A35XGRZ5niRl/d3u3B5YOo4NWsW6zDoB1Yg5x9r4PUNV6mr9xBl0fDlHqMaoRhI94Ik\nyRw52cS7m6sou+y/yjZrmoVHVyUwfmz/v4gGc24NBp9P5mRBM3vy7Rw+0Yjb44/xnDzRRE62jbkz\n7s4Yz/4axA6ExiYPBcWOQDpGeYUzsE2jVjBxvN+QMjPdSHqqEZ3u5ndjCAR3AzExMZSWljJu3DjO\nnDnD6NGjmTNnDq+//jrf/va3aWhooKamhnHjvtgONIFAcPeg06h4eGEK8yYn8PaOEk6X1fP8a4e5\nd8ZIHpw/9pYe6RAIbmWG7J0TzDn7v//7v1m/fj3Lly/npZde4r333mP16tX88pe/5L333kOj0bBm\nzRry8vKwWq1DtbQhoa+uhA46RgNUSiVP5KWzJmccVfY2Pj5wieKrTTS1urGZdLQ6PTjdvf0XOu7f\nX6PLUN0AqvIrXPyPX6N2u9l2/1NcjhqFApmHladIaamiTmPDtOQZ0EX41Ya2OmitBYUKrMmg6d6B\nIMlQXKulqkWDRiWTEe/EGhHaP6LdJfPebhcni73oNPBYno6ZEwZmZnmtup1/+0UxlRU+QMYa72XR\nwkiyZ1n7dZz+dC/4JJmDRxt5d0sll686USgge6aVNSsTGJt86xkdXbrSxp58O58dtNPQ5BeERsTr\nyJ3nj/G82+Mi+/u+CUddvYvPD9o5e70ToqKy83harYLJE03XOyGMpKVEotUIEUIgGChnz57lJz/5\nCRUVFajVarZt28YLL7zA9773PTQaDRaLhRdffBGz2czatWt58sknUSgUPP/88yiHYAxLIBAIuhIf\nZeAvH53KyZI63tpRzPYjVzh0rpq1ueOYkxF/y410CAS3OkMmSgRzzj506BAvvPACALm5ubz22muM\nHTuWyZMnYzL5Z0ymT5/O8ePHWbx48VAtbUgI15XQlZ6jAWqVgv1nKim71kyjw43VqGVKajRKpSKo\nQWXH/cM9ntWoC3girF4wFke7h8JLDTS2+j0ETE12Vm58Ga2zjV1567ickoECma9bC1kYWUWZ28Rv\nW6bzT0otOlkGR5W/S0Kp8QsS6u5+Cy6vgoIqHc0uFUadj8wEF3p16Lnesqs+3trupNEhMzrBb2YZ\nben/j8iOUY0/bq70J0VEeDDEtaPQSew91YpGoxhQG36w7gWfT+aTXdW8vuEiFZUulApYNNfGI/fH\n33K+C41NHj47ZGdPvp2L5Z0xnvflxpCbHU3yKB3NrW7MxruvM6Ing/ESqa13U1DUEjCmrKzpvK9e\npyQr0xxIxhg31oBGLQoigeBGyczM5I033uh1+4YNG3rd9tRTT/HUU099EcsSCASCbkwbH0PG2Cg+\nPlTO1gOXeXnLOfacrOCJvDSS44XRoUDQX4ZMlAjmnN3e3o5W679SGx0dTW1tLXV1dUGds8MxVM7Z\ng3FJdbq9NDS7iImJYN7UJDZ9fiHofnFREczJTOTZVRmoVJ1Fy8sbz3TrZGh0uNl94hor54/lgQUp\nHDxbSV1jOzHW3vcP9Xjtbh9bD5UjyTK7j16l3dU5QhHR2sLKjS8T2drC/gWrKJ44AwUyfxZ1ngWG\nasrcJn5cNxUnPlQaFTpnFa72BmS1DuOoNAyG7sW73SFzokjG6YHkGJiZokalDB5Z6PXKfLCrha37\n2lEo4OHFRlYtNKIagJnl/sP1/OdvS6msdqLWyETEtaExeboFf5wuq+fPHolAH85ZMwQej8Qnu6p5\n471yrlU5UakUrMhL4Kk1yYwcceuIES63xL5DdWzbVc2h43Z8EqhUChbcE819i+OZOysalRJe21zA\nazsqqW1sJzbIOTQQ7hQX4VDvm3lTR5CUaOFalZMTZxs5ebaJk2cbqeoiQkQaVGTPtDFtspVpm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Lubf7cgVBEIQZKshM4s+/sJi9p9p5Y08jr37SwL7THTz/YDFz8my3+/IE4aYRRYlr9WmjBpM8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\n9ShQ/fwaFe5fXag4VaxwBHk3qB+4G8ST765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at 0x7f1cfbd5eed0>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"M8H0_D4vYa49","colab_type":"text"},"cell_type":"markdown","source":[" 这只是一种可能的配置；也许还有同样能够提供理想结果的其他设置组合。请注意，总体而言，本练习重点不是查找*一种最佳*设置，而是帮助您对模型配置调整如何影响预测质量有一个直观的认识。"]},{"metadata":{"id":"QU5sLyYTqzqL","colab_type":"text","slideshow":{"slide_type":"slide"}},"cell_type":"markdown","source":[" ### 有适用于模型调整的标准启发法吗？\n","\n","这是一个常见的问题。简短的答案是，不同超参数的效果取决于数据。因此，不存在必须遵循的规则，您需要对自己的数据进行测试。\n","\n","即便如此，我们仍在下面列出了几条可为您提供指导的经验法则：\n","\n"," * 训练误差应该稳步减小，刚开始是急剧减小，最终应随着训练收敛达到平稳状态。\n"," * 如果训练尚未收敛，尝试运行更长的时间。\n"," * 如果训练误差减小速度过慢，则提高学习速率也许有助于加快其减小速度。\n","   * 但有时如果学习速率过高，训练误差的减小速度反而会变慢。\n"," * 如果训练误差变化很大，尝试降低学习速率。\n","   * 较低的学习速率和较大的步数/较大的批量大小通常是不错的组合。\n"," * 批量大小过小也会导致不稳定情况。不妨先尝试 100 或 1000 等较大的值，然后逐渐减小值的大小，直到出现性能降低的情况。\n","\n","重申一下，切勿严格遵循这些经验法则，因为效果取决于数据。请始终进行试验和验证。"]},{"metadata":{"id":"GpV-uF_cBCBU","colab_type":"text","slideshow":{"slide_type":"slide"}},"cell_type":"markdown","source":[" ## 任务 2：尝试其他特征\n","\n","使用 `population` 特征替换 `total_rooms` 特征，看看能否取得更好的效果。\n","\n","这部分不必超过 5 分钟。"]},{"metadata":{"id":"YMyOxzb0ZlAH","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[],"base_uri":"https://localhost:8080/","height":17},"outputId":"cfe90c30-130c-4b26-91fd-ea3fa968d199","executionInfo":{"status":"ok","timestamp":1520097533174,"user_tz":-480,"elapsed":969,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["# YOUR CODE HERE"],"execution_count":36,"outputs":[]},{"metadata":{"id":"ci1ISxxrZ7v0","colab_type":"text"},"cell_type":"markdown","source":[" ### 解决方案\n","\n","点击下方即可查看一种可能的解决方案。"]},{"metadata":{"id":"SjdQQCduZ7BV","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"output_extras":[{"item_id":11},{"item_id":12},{"item_id":13},{"item_id":14}],"base_uri":"https://localhost:8080/","height":975},"outputId":"1924fa12-a10c-42c1-9b41-31584a4daef8","executionInfo":{"status":"ok","timestamp":1520097591184,"user_tz":-480,"elapsed":57898,"user":{"displayName":"","photoUrl":"","userId":""}}},"cell_type":"code","source":["train_model(\n","    learning_rate=0.00002,\n","    steps=1000,\n","    batch_size=5,\n","    input_feature=\"population\"\n",")"],"execution_count":37,"outputs":[{"output_type":"stream","text":["Training model...\n","RMSE (on training data):\n","  period 00 : 225.63\n","  period 01 : 214.62\n","  period 02 : 204.67\n","  period 03 : 195.94\n","  period 04 : 188.98\n","  period 05 : 183.28\n","  period 06 : 180.10\n","  period 07 : 177.79\n","  period 08 : 176.42\n","  period 09 : 175.97\n","Model training finished.\n"],"name":"stdout"},{"output_type":"display_data","data":{"text/plain":["       predictions  targets\n","count      17000.0  17000.0\n","mean         120.9    207.3\n","std           97.1    116.0\n","min            0.3     15.0\n","25%           66.8    119.4\n","50%           98.7    180.4\n","75%          145.6    265.0\n","max         3018.7    500.0"],"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>predictions</th>\n","      <th>targets</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>17000.0</td>\n","      <td>17000.0</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>120.9</td>\n","      <td>207.3</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>97.1</td>\n","      <td>116.0</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.3</td>\n","      <td>15.0</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>66.8</td>\n","      <td>119.4</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>98.7</td>\n","      <td>180.4</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>145.6</td>\n","      <td>265.0</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>3018.7</td>\n","      <td>500.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Final RMSE (on training data): 175.97\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABCUAAAGkCAYAAAAG3J9IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhdLSUr7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CnOqCdsEM692Ko1klrPz5oLvDEUIIITyeJCWauHKbg8y8Uqz2+l+UUNTO8Tc/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\ncSTPyurtR+mRGEa/1EhvL0kQBEFoB8SpsTbQlHoQSzZksHF3HnId9RI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XptM1PpiEqEBv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\nz63RBPh3/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ROlUklcXBwAEydO5Nlnn+WRRx5h8uTJbbwyQWg+3cN9WXBFf15d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20+MKDCtwzCxI4521bgK9MkptmLT8EDMnJl60eMraeg+vrmvl411dyDKkp+hYuTyJ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\necaPH09ubi5NTU089NBDFBcX43K5uOGGG5g3bx7r169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